@article{
   Author = {Nwana, Hyacinth S.},
   Title = {User Modelling and User Adapted Interaction in an Intelligent Tutoring System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {1},
   Number = {1},
   Pages = {1-32},
   Note = {DOI 10.1007/BF00158950},
      Year = {1991} }



@article{
   Author = {Ballim, Afzal and Wilks, Yorick},
   Title = {Beliefs, Stereotypes and Dynamic Agent Modeling},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {1},
   Number = {1},
   Pages = {33-65},
   Note = {DOI 10.1007/BF00158951},
      Year = {1991} }



@article{
   Author = {Brennan, Susan E.},
   Title = {Conversation with and through Computers},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {1},
   Number = {1},
   Pages = {67-86},
   Note = {DOI 10.1007/BF00158952},
      Year = {1991} }



@article{
   Author = {Huang, Xueming and McCalla, Gordon I. and Greer, Jim E. and Neufeld, Eric},
   Title = {Revising Deductive Knowledge and Stereotypical Knowledge in a Student Model},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {1},
   Number = {1},
   Pages = {87-115},
   Note = {DOI 10.1007/BF00158953},
      Year = {1991} }



@article{
   Author = {Cohen, Robin and Song, Fei and Spencer, Bruce and Beek, Peter van},
   Title = {Exploiting Temporal and Novel Information from the User in Plan Recognition},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {1},
   Number = {2},
   Pages = {125-148},
   Note = {DOI 10.1007/BF00154475},
      Year = {1991} }



@article{
   Author = {Wu, Dekai},
   Title = {Active Acquisition of User Models: Implications for Decision-Theoretic   Dialog Planning and Plan Recognition},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {1},
   Number = {2},
   Pages = {149-172},
   Note = {DOI 10.1007/BF00154476},
      Year = {1991} }



@article{
   Author = {Retz-Schmidt, Gudula},
   Title = {Recognizing Intentions, Interactions, and Causes of Plan Failures},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {1},
   Number = {2},
   Pages = {173-202},
   Note = {DOI 10.1007/BF00154477},
      Year = {1991} }



@article{
   Author = {Kass, Robert},
   Title = {Building a User Model Implicitly from a Cooperative Advisory Dialog},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {1},
   Number = {3},
   Pages = {203-258},
   Note = {DOI 10.1007/BF00141081},
      Year = {1991} }



@article{
   Author = {Arragon, Paul van},
   Title = {Modeling Default Reasoning Using Defaults},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {1},
   Number = {3},
   Pages = {259-288},
   Note = {DOI 10.1007/BF00141082},
      Year = {1991} }



@article{
   Author = {Calistri-Yeh, Randall J.},
   Title = {Utilizing User Models to Handle Ambiguity and Misconceptions in Robust Plan Recognition},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {1},
   Number = {4},
   Pages = {289-322},
   Note = {DOI 10.1007/BF00141047},
      Year = {1991} }



@article{
   Author = {Raskutti, Bhavani and Zukerman, Ingrid},
   Title = {Generation and Selection of Likely Interpretation during Plan Recognition in Task-Oriented Consultation Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {1},
   Number = {4},
   Pages = {323-353},
   Note = {DOI 10.1007/BF00141048},
      Year = {1991} }



@article{
   Author = {Appelt, Douglas E. and Pollack, Martha E.},
   Title = {Weighted Abduction for Plan Ascription},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {2},
   Number = {1-2},
   Pages = {1-25},
      Year = {1991} }



@article{
   Author = {Eller, Rhonda and Carberry, Sandra},
   Title = {A Meta-Rule Approach to Flexible Plan Recognition in Dialogue},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {2},
   Number = {1-2},
   Pages = {27-53},
      Year = {1991} }



@article{
   Author = {Goodman, Bradley A. and Litman, Diane J.},
   Title = {On the Interaction between Plan Recognition and Intelligent Interfaces},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {2},
   Number = {1-2},
   Pages = {55-82},
      Year = {1992} }



@article{
   Author = {Mayfield, James},
   Title = {Controlling Inference in Plan Recognition},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {2},
   Number = {1-2},
   Pages = {83-115},
      Year = {1992} }



@article{
   Author = {London, Robert},
   Title = {Student Modeling to Support Multiple Instructional Approaches},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {2},
   Number = {1-2},
   Pages = {117-154},
      Year = {1992} }



@article{
   Author = {Sider, Judith Schaffer and Burger, John D.},
   Title = {Intention Structure and Extended Responses in a Portable Natural Language   Interface},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {2},
   Number = {1-2},
   Pages = {155-179},
      Year = {1992} }



@article{
   Author = {Sarner, Margaret and Carberry, Sandra},
   Title = {Generating Tailored Definitions Using a Multifaceted User Model},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {2},
   Number = {3},
   Pages = {181-210},
      Year = {1992} }



@article{
   Author = {Tattersall, Colin},
   Title = {Generating Help for Users of Application Software},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {2},
   Number = {3},
   Pages = {211-248},
      Year = {1992} }



@article{
   Author = {Doane, Stephanie M. and Mannes, Suzanne M. and Kintsch, Walter and Polson, Peter G.},
   Title = {Modeling User Action Planning: A Comprehension Based Approach},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {2},
   Number = {3},
   Pages = {249-285},
      Year = {1992} }



@article{
   Author = {Shifroni, Eyal and Shanon, Benny},
   Title = {Interactive User Modeling: An Integrative Explicit-Implicit Approach},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {2},
   Number = {4},
   Pages = {287-330},
      Year = {1992} }



@article{
   Author = {Moore, Johanna D. and Paris, C\'ecile L.},
   Title = {Exploiting User Feedback to Compensate for the Unreliability of User Models},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {2},
   Number = {4},
   Pages = {331-365},
      Year = {1992} }



@article{
   Author = {de Rosis, Fiorella and Pizzutilo, Sebastiano and Russo, A. and Berry, Dianne C. and Molina, F. J. Nicolau},
   Title = {Modeling the User Knowledge by Belief Networks},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {2},
   Number = {4},
   Pages = {367-388},
      Year = {1992} }



@article{
   Author = {Jennings, Andrew and Higuchi, Hideyuki},
   Title = {A User Model Neural Network for a Personal News Service},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {3},
   Number = {1},
   Pages = {1-25},
      Year = {1993} }



@article{
   Author = {May, Jon and Barnard, Philip J. and Blandford, Ann},
   Title = {Using Structural Descriptions of Interfaces to Automate the Modelling of   User Cognition},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {3},
   Number = {1},
   Pages = {27-64},
      Year = {1993} }



@article{
   Author = {Benyon, David},
   Title = {Adaptive Systems: A Solution to Usability Problems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {3},
   Number = {1},
   Pages = {65-87},
   Note = {DOI: 10.1007/BF01099425},
      Year = {1993} }



@article{
   Author = {Self, John},
   Title = {Model-Based Cognitive Diagnosis},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {3},
   Number = {1},
   Pages = {89-106},
      Year = {1993} }



@article{
   Author = {Niem, Lee and Fugere, Benot J. and Rondeau, Patrice and Tremblay, Richard},
   Title = {Defining the Semantics of Extended Genetic Graphs},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {3},
   Number = {2},
   Pages = {107-153},
      Year = {1993} }



@article{
   Author = {Zukerman, Ingrid and McConachy, Richard},
   Title = {Consulting a User Model to Address a User's Inferences during Content Planning},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {3},
   Number = {2},
   Pages = {155-185},
      Year = {1993} }



@article{
   Author = {Kaplan, Craig and Fenwick, Justine and Chen, James},
   Title = {Adaptive Hypertext Navigation Based on User Goals and Context},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {3},
   Number = {3},
   Pages = {193-220},
      Year = {1993} }



@article{
   Author = {Cawsey, Alison},
   Title = {User Modelling in Interactive Explanations},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {3},
   Number = {3},
   Pages = {221-247},
      Year = {1993} }



@article{
   Author = {Krause, JŸrgen and Hirschmann, Astrid and Mittermaier, Eva},
   Title = {The Intelligent Help System COMFOHELP: Towards a Solution of the Practicability Problem for User Modeling and Adaptive Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {3},
   Number = {3},
   Pages = {249-282},
   Note = {DOI 10.1007/BF01257891},
      Year = {1993} }



@article{
   Author = {Peter, Gerhard and R\"osner, Dietmar},
   Title = {User-Model-Driven Generation of Instructions},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {3},
   Number = {4},
   Pages = {289-319},
      Year = {1994} }



@article{
   Author = {Quilici, Alex},
   Title = {Forming User Models by Understanding User Feedback},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {3},
   Number = {4},
   Pages = {321-358},
      Year = {1994} }



@article{
   Author = {Kobsa, Alfred and Pohl, Wolfgang},
   Title = {Workshop on Adaptivity and User Modeling in Interactive Software Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {3},
   Number = {4},
   Pages = {359-367},
      Year = {1994} }



@article{
   Author = {Boyle, Craig and Encarnacion, Antonio O.},
   Title = {MetaDoc: An Adaptive Hypertext Reading System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {1},
   Pages = {1-19},
      Year = {1994} }



@article{
   Author = {Beaumont, Ian},
   Title = {User Modeling in the Interactive Anatomy Tutoring System ANATOM-TUTOR},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {1},
   Pages = {21-45},
      Year = {1994} }



@article{
   Author = {Peter, Gerhard},
   Title = {Book review: Detlef Haaks: Anpa§bare Informationssysteme Š Auf dem Weg zu aufgaben- und benutzerorientierter Systemgestaltung und FunktionalitŠt},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {1},
   Pages = {47-49},
      Year = {1994} }



@article{
   Author = {Malinowski, Uwe},
   Title = {Book review: Allen Cypher, ed.: Watch What I Do - Programming by   Demonstration; Bonnie A. Nardi: A Small Matter of Programming - Perspectives   on End User Programming},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {1},
   Pages = {50-53},
      Year = {1994} }



@article{
   Author = {Kobsa, Alfred and Pohl, Wolfgang},
   Title = {The BGP-MS User Modeling System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {2},
   Pages = {59-106},
   Note = {DOI: 10.1007/BF01099428},
      Year = {1995} }



@article{
   Author = {Orwant, Jon},
   Title = {Heterogenous Learning in the DoppelŠnger User Modeling System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {2},
   Pages = {107-130},
   Note = {DOI: 10.1007/BF01099429},
      Year = {1995} }



@article{
   Author = {Thomas, Christoph},
   Title = {ABIS-94: GI Workshop on Adaptivity and User Modeling in Interactive Software   Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {2},
   Pages = {131-138},
      Year = {1995} }



@article{
   Author = {Benyon, David},
   Title = {Book review: Mark T. Maybury, ed.: Intelligent Multimedia Interfaces},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {2},
   Pages = {139-141},
      Year = {1995} }



@article{
   Author = {Kay, Judy},
   Title = {The um Toolkit for Reusable, Long Term User Models},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {3},
   Pages = {149-196},
   Note = {DOI: 10.1007/BF01100243},
      Year = {1995} }



@article{
   Author = {Paiva, Ana and Self, John},
   Title = {TAGUS -- A User and Learner Modeling Workbench},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {3},
   Pages = {197-226},
   Note = {DOI: 10.1007/BF01100244},
      Year = {1995} }



@article{
   Author = {Carbonaro, Antonella and Maniezzo, Vittorio and Roccetti, Marco and Salomoni, Paola},
   Title = {Modelling the Student in Pitagora 2.0},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {4},
   Pages = {233-251},
      Year = {1995} }



@article{
   Author = {Corbett, Albert T. and Anderson, John R.},
   Title = {Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {4},
   Pages = {253-278},
      Year = {1995} }



@article{
   Author = {Kashihara, Akihiro and Hirashima, Tsukasa and Toyoda, Jun'ichi},
   Title = {A Cognitive Load Application in Tutoring},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {4},
   Number = {4},
   Pages = {279-303},
      Year = {1995} }



@article{
   Author = {Shute, Valerie J.},
   Title = {SMART: Student Modeling Approach for Responsive Tutoring},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {5},
   Number = {1},
   Pages = {1-44},
      Year = {1995} }



@article{
   Author = {Bull, Susan and Brna, Paul and Pain, Helen},
   Title = {Extending the Scope of Student Models},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {5},
   Number = {1},
   Pages = {45-65},
      Year = {1995} }



@article{
   Author = {Eliot, Chris and Woolf, Beverly Park},
   Title = {An Adaptive Student Centered Curriculum for an Intelligent Training System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {5},
   Number = {1},
   Pages = {67-86},
      Year = {1995} }



@article{
   Author = {Ragnemalm, Eva L.},
   Title = {Student Diagnosis in Practice: Bridging a Gap},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {5},
   Number = {2},
   Pages = {93-116},
      Year = {1996} }



@article{
   Author = {Webb, Geoffrey I. and Kuzmycz, Mark},
   Title = {Feature Based Modelling: A Methodology for Producing Coherent, Consistent, Dynamically Changing Models of Agent's Competencies},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {5},
   Number = {2},
   Pages = {117-150},
      Year = {1996} }



@article{
   Author = {Kay, Judy},
   Title = {Book review: Jim E. Greer and Gordon I. McCalla: Student Modelling: The Key to Individualized Knowledge-Based Instruction},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {5},
   Number = {2},
   Pages = {151-155},
      Year = {1996} }



@article{
   Author = {Ardissono, Liliana and Sestero, Dario},
   Title = {Using Dynamic User Models in the Recognition of the Plans of the User},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {5},
   Number = {2},
   Pages = {157-190},
      Year = {1996} }



@article{
   Author = {Jameson, Anthony},
   Title = {Numerical Uncertainty Management in User and Student Modeling: An Overview of Systems and Issues},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {5},
   Number = {3-4},
   Pages = {193-251},
      Year = {1996} }



@article{
   Author = {Mislevy, Robert J. and Gitomer, Drew H.},
   Title = {The Role of Probability-Based Inference in an Intelligent Tutoring System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {5},
   Number = {3-4},
   Pages = {253-282},
      Year = {1996} }



@article{
   Author = {Desmarais, Michael C. and Maluf, Ameen and Liu, Jiming},
   Title = {User-Expertise Modeling with Empirically Derived Probabilistic Implication   Networks},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {5},
   Number = {3-4},
   Pages = {283-315},
      Year = {1996} }



@article{
   Author = {Bauer, Mathias},
   Title = {A Dempster-Shafer Approach to Modeling Agent Preferences for Plan Recognition},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {5},
   Number = {3-4},
   Pages = {317-348},
      Year = {1996} }



@article{
   Author = {Popp, Herbert and Lšdel, Dieter},
   Title = {Fuzzy Techniques and User Modeling in Sales Assistants},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {5},
   Number = {3-4},
   Pages = {349-370},
      Year = {1996} }



@article{
   Author = {Debevc, Matjaz and Meyer, Beth and Donlagic, Dali and Svecko, Rajko},
   Title = {Design and Evaluation of an Adaptive Icon Toolbar},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {1},
   Pages = {1-21},
   Note = {DOI: 10.1007/BF00126652},
      Year = {1996} }



@article{
   Author = {Taylor, Jasper A. and Carletta, Jean and Mellish, Chris},
   Title = {Requirements for Belief Models in Cooperative Dialogue},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {1},
   Pages = {23-68},
   Note = {DOI 10.1007/BF00126653},
      Year = {1996} }



@article{
   Author = {Malinowski, Uwe},
   Title = {ABIS-95 -- GI Workshop on Adaptivity and User Modeling in Interactive   Software Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {1},
   Pages = {69-76},
      Year = {1996} }



@article{
   Author = {Vassileva, Julita},
   Title = {Book review: C\'ecile Paris: User Modeling in Text Generation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {1},
   Pages = {77-80},
      Year = {1996} }



@article{
   Author = {Beaumont, Ian},
   Title = {Book review: Alison Cawsey: Explanation and Interaction},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {1},
   Pages = {81-82},
      Year = {1996} }



@article{
   Author = {Brusilovsky, Peter},
   Title = {Methods and Techniques of Adaptive Hypermedia},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {2-3},
   Pages = {87-129},
      Year = {1996} }



@article{
   Author = {Hohl, Hubertus and B\"ocker, Heinz-Dieter and Gunzenh\"auser, Rul},
   Title = {Hypadapter: An Adaptive Hypertext System for Exploratory Learning and   Programming},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {2-3},
   Pages = {131-156},
      Year = {1996} }



@article{
   Author = {Hššk, Kristina and Karlgren, Jussi and W¾rn, Annika and DahlbŠck, Nils and Jansson, Carl Gustaf and Karlgren, Klas and Lemaire, Beno”t},
   Title = {A Glass Box Approach to Adaptive Hypermedia},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {2-3},
   Pages = {157-184},
      Year = {1996} }



@article{
   Author = {Vassileva, Julita},
   Title = {A Task-Centered Approach for User Modeling in a Hypermedia Office   Documentation System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {2-3},
   Pages = {185-224},
      Year = {1996} }



@article{
   Author = {MathŽ, Nathalie and Chen, James R.},
   Title = {User-Centered Indexing for Adaptive Information Access},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {2-3},
   Pages = {225-261},
      Year = {1996} }



@article{
   Author = {Jones, Karen Sparck},
   Title = {Book review: Ronnie W. Smith and D. Richard Hipp: Spoken Natural Language Dialogue Systems: A Practical Approach},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {2-3},
   Pages = {263-266},
      Year = {1996} }



@article{
   Author = {Mitrovic, Antonija and Djordjevic-Kajan, Slobodanka and Stoimenov, Leonid},
   Title = {INSTRUCT: Modeling Students by Asking Questions},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {4},
   Pages = {273-302},
      Year = {1996} }



@article{
   Author = {Milne, Sue and Shiu, Edward and Cook, Jean},
   Title = {Development of a Model of User Attributes and Its Implementation Within an   Adaptive Tutoring System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {4},
   Pages = {303-335},
      Year = {1996} }



@article{
   Author = {Carberry, Sandra},
   Title = {Book review: Johanna Moore: Participating in Explanatory Dialogues},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {6},
   Number = {4},
   Pages = {337-340},
      Year = {1996} }



@article{
   Author = {Horacek, Helmut},
   Title = {A Model for Adapting Explanations to the User's Likely Inferences},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {1},
   Pages = {1-55},
      Year = {1997} }



@article{
   Author = {Lindner, Hans-G\"unther},
   Title = {ABIS-96: GI Workshop on Adaptivity and User Modeling in Interactive Software   Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {1},
   Pages = {57-62},
      Year = {1997} }



@article{
   Author = {Desmarais, Michael},
   Title = {Book review: Thomas K. Landauer: The Trouble with Computers: Usefulness,   Usability and Productivity},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {1},
   Pages = {63-64},
      Year = {1997} }



@article{
   Author = {Cerri, Stefano A. and Loia, Vicenco},
   Title = {A Concurrent, Distributed Architecture for Diagnostic Reasoning},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {2},
   Pages = {69-105},
      Year = {1997} }



@article{
   Author = {Gertner, Abigail S.},
   Title = {Plan Recognition and Evaluation for On-line Critiquing},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {2},
   Pages = {107-150},
      Year = {1997} }



@article{
   Author = {Oard, Douglas W.},
   Title = {The State of the Art in Text Filtering},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {3},
   Pages = {141-178},
      Year = {1997} }



@article{
   Author = {Raskutti, Bhavani and Beitz, Anthony and Ward, Belinda},
   Title = {A Feature-Based Approach to Recommending Selections Based on Past   Preferences},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {3},
   Pages = {179-218},
      Year = {1997} }



@article{
   Author = {Appelt, Douglas E.},
   Title = {Book review: Jana Kšhler: Wiederverwendung von PlŠnen in deduktiven   Planungssystemen (Reuse of Plans in Deductive Planning Systems)},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {3},
   Pages = {219-222},
      Year = {1997} }



@article{
   Author = {Newell, Sima},
   Title = {User Models and Filtering Agents for Improved Internet Information Retrieval},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {4},
   Pages = {223-237},
      Year = {1997} }



@article{
   Author = {Hirashima, Tsukasa and Kashihara, Akihiro and Toyoda, Jun'ichi},
   Title = {Information Filtering using User's Context on Browsing in Hypertext},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {4},
   Pages = {239-256},
      Year = {1997} }



@article{
   Author = {Calvi, Licia and Bra, Paul de},
   Title = {Proficiency-Adapted Information Browsing and Filtering in Hypermedia Educational Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {4},
   Pages = {257-277},
      Year = {1997} }



@article{
   Author = {Alspector, Joshua and Kolcz, Aleksander and Karunanithi, Nachinuthu},
   Title = {Feature-Based and Clique-Based User Models for Movie Selection: A Comparative Study},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {4},
   Pages = {279-304},
      Year = {1997} }



@article{
   Author = {SchŠfer, Ralph and Bauer, Mathias},
   Title = {ABIS-97: GI Workshop on Adaptivity and User Modeling in Interactive Software   Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {4},
   Pages = {305-314},
      Year = {1997} }



@article{
   Author = {Sasikumar, Mukundan},
   Title = {Book review: Monte Zweben and Mark S Fox: Intelligent Scheduling},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {7},
   Number = {4},
   Pages = {315-318},
      Year = {1997} }



@article{
   Author = {Albrecht, David W. and Zukerman, Ingrid and Nicholson, Anne E.},
   Title = {Bayesian Models for Keyhole Plan Recognition in an Adventure Game},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {8},
   Number = {1-2},
   Pages = {5-47},
      Year = {1998} }



@article{
   Author = {Gmytrasiewicz, Piotr J. and Noh, Sanguk and Kellogg, Tad},
   Title = {Bayesian Update of Recursive Agent Models},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {8},
   Number = {1-2},
   Pages = {49-69},
      Year = {1998} }



@article{
   Author = {Balabanovic, Marko},
   Title = {Exploring versus Exploiting when Learning User Models for Text   Recommendation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {8},
   Number = {1-2},
   Pages = {71-102},
      Year = {1998} }



@article{
   Author = {Sison, Raymund C. and Numao, Masayuki and Shimura, Masamichi},
   Title = {Discovering Error Classes from Discrepancies in Novice Behaviors Via   Multistrategy Conceptual Clustering},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {8},
   Number = {1-2},
   Pages = {103-129},
      Year = {1998} }



@article{
   Author = {Chiu, Bark Cheung and Webb, Geoffrey I.},
   Title = {Using Decision Trees for Agent Modeling: Improving Prediction Performance},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {8},
   Number = {1-2},
   Pages = {131-152},
      Year = {1998} }



@article{
   Author = {Rizzo, Antonio and Palmonari, Marco},
   Title = {Book review: Bonnie A. Nardi: Activity Theory and Human-Computer Interaction},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {8},
   Number = {1-2},
   Pages = {153-157},
      Year = {1998} }



@article{
   Author = {Sasikumar, M.},
   Title = {Book review: Janet Kolodner: Case Based Reasoning},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {8},
   Number = {1-2},
   Pages = {157-160},
      Year = {1998} }



@article{
   Author = {Cohen, Robin and Allaby, Coralee and Cumbaa, Christian and Fitzgerald, Mark and Ho, Kinson and Hui, Bowen and Latulipe, Celine and Lu, Fletcher and Moussa, Nancy and Pooley, David and Qian, Alex and Siddiqi, Saheem},
   Title = {What is Initiative?},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {8},
   Number = {3-4},
   Pages = {171-214},
      Year = {1998} }



@article{
   Author = {Chu-Carroll, Jennifer and Brown, Michael K.},
   Title = {An Evidential Model for Tracking Initiative in Collaborative Dialogue   Interactions},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {8},
   Number = {3-4},
   Pages = {215-253},
      Year = {1998} }



@article{
   Author = {Guinn, Curry I.},
   Title = {An Analysis of Initiative Selection in Collaborative Task-Oriented Discourse},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {8},
   Number = {3-4},
   Pages = {255-314},
      Year = {1998} }



@article{
   Author = {Rich, Charles and Sidner, Candace L.},
   Title = {COLLAGEN: A Collaboration Manager for Software Interface Agents},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {8},
   Number = {3-4},
   Pages = {315-350},
      Year = {1998} }



@article{
   Author = {Lester, James C. and Stone, Brian A. and Stelling, Gary D.},
   Title = {Lifelike Pedagogical Agents for Mixed-Initiative Problem Solving in Constructivist Learning Environments},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {9},
   Number = {1-2},
   Pages = {1-44},
      Year = {1999} }



@article{
   Author = {Cesta, Amedeo and D'Aloisi, Daniela},
   Title = {Mixed-Initiative Issues in an Agent-Based Meeting Scheduler},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {9},
   Number = {1-2},
   Pages = {45-78},
      Year = {1999} }



@article{
   Author = {Ishizaki, M. and Crocker, M. and Mellish, C.},
   Title = {Mixed-Initiative Dialogue Using Computer Dialogue Simulation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {9},
   Number = {1-2},
   Pages = {79-91},
      Year = {1999} }



@article{
   Author = {Green, Nancy and Carberry, Sandra},
   Title = {A Computational Mechanism for Initiative in Answer Generation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {9},
   Number = {1-2},
   Pages = {93-132},
      Year = {1999} }



@article{
   Author = {Stein, Adelheit and Gulla, Jon Atle and Thiel, Ulrich},
   Title = {User-Tailored Planning of Mixed-Initiative Information-Seeking Dialogues},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {9},
   Number = {1-2},
   Pages = {133-166},
      Year = {1999} }



@article{
   Author = {Hagen, Eli},
   Title = {An Approach to Mixed Initiative Spoken Information Retrieval Dialogue},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {9},
   Number = {1-2},
   Pages = {167-213},
      Year = {1999} }



@article{
   Author = {Pohl, Wolfgang},
   Title = {Logic-Based Representation and Reasoning for User Modeling Shell Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {9},
   Number = {3},
   Pages = {217-282},
   Note = {DOI 10.1023/A:1008325713804},
      Year = {1999} }



@article{
   Author = {Lascio, Luigi Di and Fischetti, Enrico and Gisolfi, Antonio},
   Title = {A Fuzzy-Based Approach to Stereotype Selection in Hypermedia},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {9},
   Number = {4},
   Pages = {285-320},
      Year = {1999} }



@article{
   Author = {Virvou, Maria and Bulay, Benedict du},
   Title = {Human Plausible Reasoning for Intelligent Help},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {9},
   Number = {4},
   Pages = {321-375},
      Year = {1999} }



@article{
   Author = {Doane, Stephanie},
   Title = {ADAPT: A Predictive Cognitive Model of User Visual Attention and Action Planning},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {10},
   Number = {1},
      Year = {2000} }



@article{
   Author = {Cawsey, Alison J. and Jones, Ray B. and Pearson, Janne},
   Title = {The Evaluation of a Personalised Health Information System for Patients with Cancer},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {10},
   Number = {1},
      Year = {2000} }



@article{
   Author = {de Rosis, Fiorella},
   Title = {Book review: Mark Maybury: Intelligent Multimedia Information Retrieval},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {10},
   Number = {1},
      Year = {2000} }



@article{
   Author = {Corbett, Albert and McLaughlin, Megan and Scarpinatto, K. Christine},
   Title = {Modeling Student Knowledge: Cognitive Tutors in High School and College},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {10},
   Number = {2-3},
   Pages = {81-108},
      Year = {2000} }



@article{
   Author = {Strachan, Linda and Anderson, John and Sneesby, Murray and Evans, Mark},
   Title = {Minimalist User Modelling in a Complex Commercial Software System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {10},
   Number = {2-3},
   Pages = {109-146},
      Year = {2000} }



@article{
   Author = {Billsus, Daniel and Pazzani, Michael J.},
   Title = {User Modeling for Adaptive News Access},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {10},
   Number = {2-3},
   Pages = {147-180},
   Note = {DOI 10.1023/A:1026501525781},
      Year = {2000} }



@article{
   Author = {Linton, Frank and Schaefer, Hans-Peter},
   Title = {Recommender Systems for Learning: Building User and Expert Models through Long-Term Observation of Application Use},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {10},
   Number = {2-3},
   Pages = {181-208},
      Year = {2000} }



@article{
   Author = {Fink, Josef and Kobsa, Alfred},
   Title = {A Review and Analysis of Commercial User Modeling Servers for Personalization on the World Wide Web},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {10},
   Number = {2-3},
   Pages = {209-249},
   Note = {DOI: 10.1023/A:1026597308943},
      Year = {2000} }



@article{
   Author = {Kobsa, Alfred},
   Title = {Generic User Modeling Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {1-2},
   Pages = {49-63},
   Note = {DOI: 10.1023/A:1011187500863},
      Year = {2001} }



@article{
   Author = {Ardissono, Liliana and Goy, Anna},
   Title = {Tailoring the Interaction with Users in Web Stores},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {10},
   Number = {4},
   Pages = {251-303},
      Year = {2000} }



@article{
   Author = {Shapira, Bracha and Shoval, Peretz and Hanani, Uri},
   Title = {Information Filtering: Overview of Issues, Research and Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {3},
   Pages = {203-259},
      Year = {2001} }



@article{
   Author = {Brusilovsky, Peter},
   Title = {Adaptive Hypermedia},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {1-2},
   Pages = {87-110},
      Year = {2001} }



@article{
   Author = {Carberry, Sandra},
   Title = {Techniques for Plan Recognition},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {1-2},
   Pages = {31-48},
      Year = {2001} }



@article{
   Author = {Chin, David},
   Title = {Empirical Evaluation of User Models and User-Adapted Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {1-2},
   Pages = {181-194},
   Note = {DOI 10.1023/A:1011127315884},
      Year = {2001} }



@article{
   Author = {Fischer, Gerhard},
   Title = {User Modeling in Human-Computer Interaction},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {1-2},
   Pages = {65-86},
      Year = {2001} }



@article{
   Author = {Kay, Judy},
   Title = {Learner Control},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {1-2},
   Pages = {111-127},
      Year = {2001} }



@article{
   Author = {Kobsa, Alfred},
   Title = {Preface},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {1-2},
   Pages = {1-4},
   Note = {DOI: 10.1023/A:1011191716506},
      Year = {2001} }



@article{
   Author = {Stephanidis, Costas},
   Title = {Adaptive Techniques for Universal Access},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {1-2},
   Pages = {159-179},
   Note = {DOI 10.1023/A:1011144232235},
      Year = {2001} }



@article{
   Author = {Webb, Geoffrey I. and Pazzani, Michael J. and Billsus, Daniel},
   Title = {Machine Learning for User Modeling},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {1-2},
   Pages = {11-21},
      Year = {2001} }



@article{
   Author = {Zukerman, Ingrid and Albrecht, David W.},
   Title = {Predictive Statistical Models for User Modeling},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {1-2},
   Pages = {5-18},
      Year = {2001} }



@article{
   Author = {Zukerman, Ingrid and Litman, Diane},
   Title = {Natural Language Processing and User Modeling: Synergies and Limitations},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {1-2},
   Pages = {129-158},
      Year = {2001} }



@article{
   Author = {Strohecker, Carol},
   Title = {Book Review: M. Kyng and L. Mathiassen (eds.), Computers and Design in Context. Cambridge, MA: MIT Press, 1997.},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {11},
   Number = {3},
   Pages = {261-266},
      Year = {2001} }



@article{
   Author = {Hudlicka, Eva and McNeese, Michael D.},
   Title = {Assessment of User Affective and Belief States for Interface Adaptation: Application to an Air Force Pilot Task},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {1},
   Pages = {1-47},
      Year = {2002} }



@article{
   Author = {Bianchi-Berthouze, Nadia and Lisetti, Christine L.},
   Title = {Modeling Multimodal Expression of User's Affective Subjective Experience},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {1},
   Pages = {49-84},
      Year = {2002} }



@article{
   Author = {Waern, Annika},
   Title = {Book Review: Rosalind Picard: Affective Computing; Kerstin Dautenhahn, ed.: Human Cognition and Social Agent Technology},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {1},
   Pages = {85-89},
      Year = {2002} }



@article{
   Author = {Hššk, Kristina},
   Title = {Book Review: Ana Paiva (ed.): Affective Interactions: Towards a New Generation of Computer Interfaces Ź},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {1},
   Pages = {91-96},
      Year = {2002} }



@article{
   Author = {Chin, David N. and Crosby, Martha E.},
   Title = {Introduction to the Special Issue on Empirical Evaluation of User Models and User Modeling Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {2-3},
   Pages = {105-109ŹŹ},
      Year = {2002} }



@article{
   Author = {Litman, Diane J. and Pan, Shimei},
   Title = {Designing and Evaluating an Adaptive Spoken Dialogue System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {2-3},
   Pages = {111-137ŹŹ},
      Year = {2002} }



@article{
   Author = {Keates, Simeon and Langdon, Patrick and Clarkson, P. John and Robinson, Peter},
   Title = {User Models and User Physical Capability},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {2-3},
   Pages = {139-169ŹŹ},
      Year = {2002} }



@article{
   Author = {Sohn, Young Woo and Doane, Stephanie M.},
   Title = {Evaluating Comprehension-Based User Models: Predicting Individual User Planning and Action},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {2-3},
   Pages = {171-205ŹŹ},
      Year = {2002} }



@article{
   Author = {Barker, Trevor and Jones, Sara and Britton, Carol and Messer, David},
   Title = {The Use of a Co-operative Student Model of Learner Characteristics to Configure a Multimedia Application},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {2-3},
   Pages = {207-241ŹŹ},
      Year = {2002} }



@article{
   Author = {Mitrovic, Antonija and Martin, Brent and Mayo, Michael},
   Title = {Using Evaluation to Shape ITS Design: Results and Experiences with SQL-Tutor},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {2-3},
   Pages = {243-279ŹŹ},
      Year = {2002} }



@article{
   Author = {Mill‡n, Eva and PŽrez-de-la-Cruz, JosŽ Luis},
   Title = {A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {3-4},
   Pages = {281-330ŹŹ},
      Year = {2002} }



@article{
   Author = {Burke, Robin},
   Title = {Hybrid Recommender Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {4},
   Pages = {331-370Ź},
      Year = {2002} }



@article{
   Author = {Conati, Cristina and Gertner, Abigail and VanLehn, Kurt},
   Title = {Using Bayesian Networks to Manage Uncertainty in Student Modeling},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {4},
   Pages = {371-417},
      Year = {2002} }



@article{
   Author = {Nardi, Bonnie A.},
   Title = {Book Review: User Interfaces for All: Concepts, Methods and Tools},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {12},
   Number = {4},
   Pages = {419-420},
      Year = {2002} }



@article{
   Author = {Alpert, S.R. and Karat, J. and Karat, C-M. and Brodie, C. and Vergo, J.G.},
   Title = {User Attitudes Regarding a User-Adaptive eCommerce Web Site},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {13},
   Number = {4},
   Pages = {373-396},
   Note = {DOI 10.1023/A:1026201108015},
      Year = {2003} }



@article{
   Author = {Papanikolaou, Kyparisia A. and Maria Grigoriadou, Harry Kornilakis and Magoulas, George D.},
   Title = {Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {13},
   Number = {3},
   Pages = {213-267ŹŹ},
      Year = {2003} }



@article{
   Author = {Bunt, Andrea and Conati, Cristina},
   Title = {Probabilistic Student Modelling to Improve Exploratory Behaviour},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {13},
   Number = {3},
   Pages = {269-309},
      Year = {2003} }



@article{
   Author = {Mukherjee, Rajatish and Sajja, Neelima and Sen, Sandip},
   Title = {A Movie Recommendation System: An Application of Voting Theory in User Modeling},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {13},
   Number = {1+2},
   Pages = {5-33},
      Year = {2003} }



@article{
   Author = {Eliassi-Rad, Tina and Shavlik, Jude},
   Title = {A System for Building Intelligent Agents that Learn to Retrieve and Extract Information},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {13},
   Number = {1+2},
   Pages = {35-88},
      Year = {2003} }



@article{
   Author = {Cassell, Justine and Bickmore, Timothy},
   Title = {Negotiated Collusion: Modeling Social Languageand its Relationship Effects in Intelligent Agents},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {13},
   Number = {1+2},
   Pages = {89-132},
      Year = {2003} }



@article{
   Author = {Thom, Belinda},
   Title = {Interactive Improvisational Music Companionship: A User-Modeling Approach},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {13},
   Number = {1+2},
   Pages = {133-177},
      Year = {2003} }



@article{
   Author = {Vassileva, Julita and McCalla, Gordon and Greer, Jim},
   Title = {Multi-Agent Multi-User Modeling in I-Help},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {13},
   Number = {1+2},
   Pages = {179-210},
   Note = {DOI: 10.1023/A:1024072706526},
      Year = {2003} }



@article{
   Author = {O'Sullivan, Derry and Smyth, Barry and Wilson, David C. and McDonald, Kieran and Smeaton, Alan},
   Title = {Improving the Quality of the Personalized Electronic Program Guide},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {1},
   Pages = {5-36},
      Year = {2004} }



@article{
   Author = {Masthoff, Judith},
   Title = {Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {1},
   Pages = {37-85},
      Year = {2004} }



@article{
   Author = {Hara, Yumiko and Tomomune, Yumiko and Shigemori, Maki},
   Title = {Categorization of Japanese TV Viewers Based on Program Genres They Watch},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {1},
   Pages = {87-117},
      Year = {2004} }



@article{
   Author = {Maybury, Mark and Greiff, Warren and Boykin, Stanley and Ponte, Jay and McHenry, Chad and Ferro, Lisa},
   Title = {Personalcasting: Tailored Broadcast News},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {1},
   Pages = {119-144},
      Year = {2004} }



@article{
   Author = {Micarelli, Alessandro and Sciarrone, Filippo},
   Title = {Anatomy and Empirical Evaluation of an Adaptive Web-Based Information Filtering System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {2-3},
   Pages = {159-200},
   Note = {http://dx.doi.org/10.1023/B:USER.0000028981.43614.94},
      Year = {2004} }



@article{
   Author = {W¾rn, Annika},
   Title = {User Involvement in Automatic Filtering: An Experimental Study},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {2-3},
   Pages = {201-237},
   Note = {http://dx.doi.org/10.1023/B:USER.0000028984.13876.9b},
      Year = {2004} }



@article{
   Author = {Magnini, Bernardo and Strapparava, Carlo},
   Title = {User Modelling for News Web Sites with Word Sense Based Techniques},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {2-3},
   Pages = {239-257},
   Note = {http://dx.doi.org/10.1023/B:USER.0000028980.13669.44},
      Year = {2004} }



@article{
   Author = {Leuski, Anton and Allan, James},
   Title = {Interactive Information Retrieval Using Clustering and Spatial Proximity},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {2-3},
   Pages = {259-288},
   Note = {http://dx.doi.org/10.1023/B:USER.0000028978.09823.47},
      Year = {2004} }



@article{
   Author = {Tsiriga, Victoria and Virvou, Maria},
   Title = {A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {4},
   Pages = {289-316},
   Note = {http://dx.doi.org/10.1023/B:USER.0000043396.14788.cc},
      Year = {2004} }



@article{
   Author = {Michaud, Lisa N. and McCoy, Kathleen F.},
   Title = {Empirical Derivation of a Sequence of User Stereotypes for Language Learning},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {4},
   Pages = {317-350},
   Note = {http://dx.doi.org/10.1023/B:USER.0000043398.04349.b7},
      Year = {2004} }



@article{
   Author = {Soller, Amy},
   Title = {Computational Modeling and Analysis of Knowledge Sharing in Collaborative Distance Learning},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {4},
   Pages = {351-381},
   Note = {http://dx.doi.org/10.1023/B:USER.0000043436.49168.3b},
      Year = {2004} }



@article{
   Author = {Smyth, Barry and Balfe, Evelyn and Freyne, Jill and Briggs, Peter and Coyle, Maurice and Boydell, Oisin},
   Title = {Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {5},
   Pages = {383-423},
   Note = {http://dx.doi.org/10.1007/s11257-004-5270-4},
      Year = {2004} }



@article{
   Author = {Romero, Crist—bal and Ventura, Sebastian and De Bra, Paul},
   Title = {Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {5},
   Pages = {425-464},
   Note = {http://dx.doi.org/10.1007/s11257-004-7961-2},
      Year = {2004} }



@article{
   Author = {Carberry, Sandra and Zukerman, Ingrid},
   Title = {Preface to the Special Issue on Language-Based Interaction},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {1-2},
   Pages = {1-3},
   Note = {http://dx.doi.org/10.1007/s11257-004-7817-9},
      Year = {2005} }



@article{
   Author = {Zukerman, Ingrid and George, Sarah},
   Title = {A Probabilistic Approach for Argument Interpretation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {1-2},
   Pages = {5-53},
   Note = {http://dx.doi.org/10.1007/s11257-004-5660-7},
      Year = {2005} }



@article{
   Author = {Michaud, Lisa N. and McCoy, Kathleen F. and Davis, Rashida Z.},
   Title = {A Model to Disambiguate Natural Language Parses on the Basis of User Language Proficiency: Design and Evaluation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {1-2},
   Pages = {55-84},
   Note = {http://dx.doi.org/10.1007/s11257-004-6262-0},
      Year = {2005} }



@article{
   Author = {Goodman, Bradley A. and Linton, Frank N. and Gaimari, Robert D. and Hitzeman, Janet M. and Ross, Helen J. and Zarrella, Guido},
   Title = {Using Dialogue Features to Predict Trouble During Collaborative Learning},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {1-2},
   Pages = {85-134},
   Note = {http://dx.doi.org/10.1007/s11257-004-5269-x},
      Year = {2005} }



@article{
   Author = {Bontcheva, Kalina and Wilks, Yorick},
   Title = {Tailoring Automatically Generated Hypertext},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {1-2},
   Pages = {135-168},
   Note = {http://dx.doi.org/10.1007/s11257-004-5637-6},
      Year = {2005} }



@article{
   Author = {Komatani, Kazunori and Ueno, Shinichi and Kawahara, Tatsuya and Okuno, Hiroshi G.},
   Title = {User Modeling in Spoken Dialogue Systems to Generate Flexible Guidance},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {1-2},
   Pages = {169-183},
   Note = {http://dx.doi.org/10.1007/s11257-004-5659-0},
      Year = {2005} }



@article{
   Author = {Brusilovsky, Peter and Tasso, Carlo},
   Title = {Preface to Special Issue on User Modeling for Web Information Retrieval},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {14},
   Number = {2-3},
   Pages = {147-157},
   Note = {http://dx.doi.org/10.1023/B:USER.0000029016.80122.dd},
      Year = {2004} }



@article{
   Author = {Carmichael, David J. and Kay, Judy and Kummerfeld, Bob},
   Title = {Consistent Modelling of Users, Devices and Sensors in a Ubiquitous Computing Environment},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {3-4},
   Pages = {197-234},
   Note = { DOI: 10.1007/s11257-005-0001-z},
      Year = {2005} }



@article{
   Author = {Cheverst, Keith and Byun, Hee Eon and Fitton, Dan and Sas, Corina and Kray, Chris and Villar, Nicolas},
   Title = {Exploring Issues of User Model Transparency and Proactive Behaviour in an Office Environment Control System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {3-4},
   Pages = {235-273},
   Note = {http://dx.doi.org/10.1007/s11257-005-1269-8},
      Year = {2005} }



@article{
   Author = {Zimmermann, Andreas and Specht, Marcus and Lorenz, Andreas},
   Title = {Personalization and Context Management},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {3-4},
   Pages = {275-302},
   Note = {http://dx.doi.org/10.1007/s11257-005-1092-2},
      Year = {2005} }



@article{
   Author = {Petrelli, Daniela and Not, Elena},
   Title = {User-Centred Design of Flexible Hypermedia for a Mobile Guide: Reflections on the HyperAudio Experience},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {3-4},
   Pages = {303-338},
   Note = {http://dx.doi.org/10.1007/s11257-005-8816-1},
      Year = {2005} }



@article{
   Author = {Hatala, Marek and Wakkary, Ron},
   Title = {Ontology-Based User Modeling in an Augmented Audio Reality System for Museums},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {3-4},
   Pages = {339-380},
   Note = {http://dx.doi.org/10.1007/s11257-005-2304-5},
      Year = {2005} }



@article{
   Author = {Pogacnik, Matevz and Tasic, Jurij and Meza, Marko and Kosir, Andrej},
   Title = {Personal Content Recommender based on a Hierarchical User Model for the Selection of TV Programmes},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {5},
   Pages = {425-457},
   Note = {http://dx.doi.org/10.1007/s11257-005-4065-6},
      Year = {2005} }



@article{
   Author = {Fischer, Arnout R. H. and Blommaert, Frans J. J. and Midden, Cees J. H.},
   Title = {Combining Experimental Observations and Modelling in Investigating Feedback and Emotions in Repeated Selection Tasks},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {5},
   Pages = {389-424},
   Note = {http://dx.doi.org/10.1007/s11257-005-1868-4},
      Year = {2005} }



@article{
   Author = {Tarpin-Bernard, Franck and Habieb-Mammar, Halima},
   Title = {Modeling Elementary Cognitive Abilities for Adaptive Hypermedia Presentation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {5},
   Pages = {459-495},
   Note = {http://dx.doi.org/10.1007/s11257-005-2529-3},
      Year = {2005} }



@article{
   Author = {Wilson, Roy},
   Title = {Book Review: Embodied Conversational Agents. Edited by Justine Cassell, Joseph Sullivan, Scott Prevost, and Elizabeth Churchill.},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {5},
   Pages = {497-503},
   Note = {http://dx.doi.org/10.1007/s11257-005-4543-x},
      Year = {2005} }



@article{
   Author = {Kobsa, Alfred and Fink, Josef},
   Title = {An LDAP-Based User Modeling Server and its Evaluation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {2},
   Pages = {129-169},
   Note = {http://dx.doi.org/10.1007/s11257-006-9006-5},
      Year = {2006} }



@article{
   Author = {Jameson, Anthony and KrŸger, Antonio},
   Title = {Preface to the Special Issue on User Modeling in Ubiquitous Computing},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {3-4},
   Pages = {193-195},
   Note = {http://dx.doi.org/10.1007/s11257-005-2335-y},
      Year = {2005} }



@article{
   Author = {Carberry, Sandra and Zukerman, Ingrid},
   Title = {Preface to the Special Issue on Language-Based Interaction},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {1-2},
   Pages = {1-3},
   Note = {http://dx.doi.org/10.1007/s11257-004-7817-9},
      Year = {2005} }



@article{
   Author = {Carberry, Sandra and Zukerman, Ingrid},
   Title = {Preface to the Special Issue on Language-Based Interaction},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {15},
   Number = {1-2},
   Pages = {1-3},
   Note = {http://dx.doi.org/10.1007/s11257-004-7817-9},
      Year = {2005} }



@article{
   Author = {Elzer, Stephanie and Green, Nancy and Carberry, Sandra and Hoffman, James},
   Title = {A Model of Perceptual Task Effort for Bar Charts and its Role in Recognizing Intention},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {1},
   Pages = {1-30},
   Note = {http://dx.doi.org/10.1007/s11257-006-9002-9},
      Year = {2006} }



@article{
   Author = {Goren-Bar, Dina and Graziola, Ilenia and Pianesi, Fabio and Zancanaro, Massimo},
   Title = {The Influence of Personality Factors on Visitor Attitudes Towards Adaptivity Dimensions for Mobile Museum Guides},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {1},
   Pages = {31-62},
   Note = {http://dx.doi.org/10.1007/s11257-006-9004-7},
      Year = {2006} }



@article{
   Author = {Yu, Zhiwen and Zhou, Xingshe and Hao, Yanbin and Gu, Jianhua},
   Title = {TV Program Recommendation for Multiple Viewers Based on User Profile Merging},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {1},
   Pages = {63-82},
   Note = {http://dx.doi.org/10.1007/s11257-006-9005-6},
      Year = {2006} }



@article{
   Author = {Goodman, Bradley A. and Linton, Frank N. and Gaimari, Robert D. and Hitzeman, Janet M. and Ross, Helen J. and Zarrella, Guido},
   Title = {Erratum: Using Dialogue Features to Predict Trouble During Collaborative Learning},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {1},
   Pages = {83-84},
   Note = {http://dx.doi.org/10.1007/s11257-006-9001-x},
      Year = {2006} }



@article{
   Author = {Petrelli, Daniela and Not, Elena},
   Title = {Erratum: User-Centred Design of Flexible Hypermedia for a Mobile Guide: Reflections on the HyperAudio Experience},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {1},
   Pages = {85-86},
   Note = {http://dx.doi.org/10.1007/s11257-006-9003-8},
      Year = {2006} }



@article{
   Author = {NŸckles, Matthias and Winter, Alexandra and Wittwer, Jšrg and Herbert, Markus and HŸbner, Sandra},
   Title = {How do Experts Adapt their Explanations to a LaypersonÕs Knowledge in Asynchronous Communication? An Experimental Study},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {2},
   Pages = {87-127},
   Note = {http://dx.doi.org/10.1007/s11257-006-9000-y},
      Year = {2006} }



@article{
   Author = {Gaudioso, Helena and Soller, Amy and Vassileva, Julita},
   Title = {Preface to the Special issue on User Modeling to Support Groups, Communities and Collaboration},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {3-4},
   Pages = {171-174},
   Note = {http://dx.doi.org/10.1007/s11257-006-9010-9},
      Year = {2006} }



@article{
   Author = {Harrer, Andreas and McLaren, Bruce M. and Walker, Erin and Bollen, Lars and Sewall, Jonathan},
   Title = {Creating Cognitive Tutors for Collaborative Learning: Steps toward Realization},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {3-4},
   Pages = {175-209},
   Note = {http://dx.doi.org/10.1007/s11257-006-9007-4},
      Year = {2006} }



@article{
   Author = {Suebnukarn, Siriwan and Haddawy, Peter},
   Title = {Modeling Individual and Collaborative Problem-Solving in Medical Problem-Based Learning},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {3-4},
   Note = {http://dx.doi.org/10.1007/s11257-006-9011-8},
      Year = {2006} }



@article{
   Author = {Introne, Joshua and Alterman, Richard},
   Title = {Using Shared Representations to Improve Coordination and Intent Inference},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {3-4},
   Pages = {249-280},
   Note = {http://dx.doi.org/10.1007/s11257-006-9009-2},
      Year = {2006} }



@article{
   Author = {Masthoff, Judith and Gatt, Albert},
   Title = {In Pursuit of Satisfaction and the Prevention of Embarrassment: Affective State in Group Recommender Systems},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {3-4},
   Pages = {281-319},
   Note = {http://dx.doi.org/10.1007/s11257-006-9008-3},
      Year = {2006} }



@article{
   Author = {Cheng, Ran and Vassileva, Julita},
   Title = {Design and Evaluation of an Adaptive Incentive Mechanism for Sustained Educational Online Communities},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {3-4},
   Pages = {321-348},
   Note = {http://dx.doi.org/10.1007/s11257-006-9013-6},
      Year = {2006} }



@article{
   Author = {Read, Timothy and Barros, Beatriz and B‡rcena, Elena and Pancorbo, Jesœs},
   Title = {Coalescing Individual and Collaborative Learning to Model User Linguistic Competences},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {3-4},
   Pages = {349-376},
   Note = {http://dx.doi.org/10.1007/s11257-006-9014-5},
      Year = {2006} }



@article{
   Author = {Alfonseca, Enrique and Carro, Rosa M. and Mart’n, Estefan’a and Ortigosa, Alvaro and Paredes, Pedro},
   Title = {The Impact of Learning Styles on Student Grouping for Collaborative Learning: A Case Study},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {3-4},
   Pages = {377-401},
   Note = {http://dx.doi.org/10.1007/s11257-006-9012-7},
      Year = {2006} }



@article{
   Author = {Desmarais, Michel C. and Meshkinfam, Peyman and Gagnon, Michel},
   Title = {Learned Student Models with Item to Item Knowledge Structures},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {5},
   Pages = {403-434},
   Note = {http://dx.doi.org/10.1007/s11257-006-9016-3},
      Year = {2006} }



@article{
   Author = {Rosaci, Domenico and SarnŽ, Giuseppe M. L.},
   Title = {MASHA: A Multi Agent System Handling User and Device Adaptivity of Web Sites},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {16},
   Number = {5},
   Pages = {435-462},
   Note = {http://dx.doi.org/10.1007/s11257-006-9015-4},
      Year = {2006} }



@article{
   Author = {Kobsa, Alfred},
   Title = {Editorial},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {4},
   Number = {2},
   Pages = {iii-v},
   Note = {DOI 10.1007/BF01099427},
      Year = {1995} }



@article{
   Author = {Lekakos, George and Giaglis, George},
   Title = {A hybrid approach for improving predictive accuracy of collaborative filtering algorithms},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {17},
   Number = {1+2},
   Pages = {5-40},
   Note = {DOI 10.1007/s11257-006-9019-0},
      Year = {2007} }



@article{
   Author = {Guzm‡n, Eduardo  and Conejo, Ricardo and PŽrez-de-la-Cruz, JosŽ-Luis},
   Title = {Adaptive Testing for Hierarchical Student Models},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {1+2},
   Pages = {119-157},
   Note = {DOI 10.1007/s11257-006-9018-1},
      Year = {2007} }



@article{
   Author = {Horvitz, Eric and Paek, Tim},
   Title = {Complementary Computing: Policies for Transferring Callers from Dialog Systems to Human Receptionists},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {1+2},
   Pages = {159-182},
   Note = {DOI 10.1007/s11257-006-9026-1},
      Year = {2007} }



@article{
   Author = {Hollink, Vera and van Someren, Maarten and Wielinga, Bob},
   Title = {Discovering Stages in Web Navigation for Problem-Oriented Navigation Support},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {1+2},
   Pages = {183-214},
   Note = {DOI 10.1007/s11257-006-9017-2},
      Year = {2007} }



@article{
   Author = {Domshlak, Carmel and Joachims, Thorsten},
   Title = {Efficient and Non-Parametric Reasoning over User Preferences},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {1+2},
   Pages = {41-69},
   Note = {DOI 10.1007/s11257-006-9022-5},
      Year = {2007} }



@article{
   Author = {Paek, Tim and Chickering, David},
   Title = {Improving Command and Control Speech Recognition on Mobile Devices: Using Predictive User Models for Language Modeling},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {1+2},
   Pages = {93-117},
   Note = {DOI 10.1007/s11257-006-9021-6},
      Year = {2007} }



@article{
   Author = {Albrecht, David and Zukerman, Ingrid},
   Title = {Introduction to the Special Issue on Statistical and Probabilistic Methods for User Modeling},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {1+2},
   Pages = {1-4},
   Note = {DOI 10.1007/s11257-006-9025-2},
      Year = {2007} }



@article{
   Author = {Chickering, David and Paek, Tim},
   Title = {Personalizing Influence Diagrams: Applying Online Learning Strategies to Dialogue Management},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {1+2},
   Pages = {71-91},
   Note = {DOI 10.1007/s11257-006-9020-7},
      Year = {2007} }



@article{
   Author = {Kobsa, Alfred},
   Title = {Preface},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {1},
   Number = {1},
   Pages = {v-viii},
   Note = {DOI 10.1007/BF00158949},
      Year = {1991} }



@article{
   Author = {Hollink, Vera and van Someren, Maarten and Wielinga, Bob J.},
   Title = {Navigation Behavior Models for Link Structure Optimization},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {4},
   Pages = {339-377},
   Note = {DOI 10.1007/s11257-007-9030-0},
      Year = {2007} }



@article{
   Author = {Kosba, Essam and Dimitrova, Vania and Boyle, Roger},
   Title = {Adaptive Feedback Generation to Support Teachers in Web-based Distance Education},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {4},
   Pages = {379-413},
   Note = {DOI 10.1007/s11257-007-9031-z},
      Year = {2007} }



@article{
   Author = {Serna, Audrey and Pigot, HŽlne and Rialle, Vincent},
   Title = {Modeling the Progression of AlzheimerÕs Disease for Cognitive Assistance in Smart Homes},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {4},
   Pages = {415-438},
   Note = {DOI 10.1007/s11257-007-9032-y},
      Year = {2007} }



@article{
   Author = {Degemmis, Marco and Lops, Pasquale and Semeraro, Giovanni},
   Title = {A Content-Collaborative Recommender that Exploits WordNet-based User Profiles for Neighborhood Formation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {3},
   Pages = {217-255},
   Note = {DOI 10.1007/s11257-006-9023-4},
   Abstract = {Abstract  Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. Traditional collaborative approaches compute a similarity value between the current user and each other user by taking into account their rating style, that is the set of ratings given on the same items. Based on the ratings of the most similar users, commonly referred to as neighbors, collaborative algorithms compute recommendations for the current user. The problem with this approach is that the similarity value is only computable if users have common rated items. The main contribution of this work is a possible solution to overcome this limitation. We propose a new content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles. In more detail, user profiles are clustered to discover current user neighbors. Content-based user profiles play a key role in the proposed hybrid recommender. Traditional keyword-based approaches to user profiling are unable to capture the semantics of user interests. A distinctive feature of our work is the integration of linguistic knowledge in the process of learning semantic user profiles representing user interests in a more effective way, compared to classical keyword-based profiles, due to a sense-based indexing. Semantic profiles are obtained by integrating machine learning algorithms for text categorization, namely a nave Bayes approach and a relevance feedback method, with a word sense disambiguation strategy based exclusively on the lexical knowledge stored in the WordNet lexical database. Experiments carried out on a content-based extension of the EachMovie dataset show an improvement of the accuracy of sense-based profiles with respect to keyword-based ones, when coping with the task of classifying movies as interesting (or not) for the current user. An experimental session has been also performed in order to evaluate the proposed hybrid recommender system. The results highlight the improvement in the predictive accuracy of collaborative recommendations obtained by selecting like-minded users according to user profiles.},
      Year = {2007} }



@article{
   Author = {Stock, Oliviero and Zancanaro, Massimo and Busetta, Paolo and Callaway, Charles and KrŸger, Antonio and Kruppa, Michael and Kuflik, Tsvi and Not, Elena and Rocchi, Cesare},
   Title = {Adaptive, Intelligent Presentation of Information for the Museum Visitor in PEACH},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {3},
   Pages = {257-304},
   Note = {DOI 10.1007/s11257-007-9029-6},
   Abstract = {The study of intelligent user interfaces and user modeling and adaptation is well suited for augmenting educational visits to museums. We have defined a novel integrated framework for museum visits and claim that such a framework is essential in such a vast domain that inherently implies complex interactivity. We found that it requires a significant investment in software and hardware infrastructure, design and implementation of intelligent interfaces, and a systematic and iterative evaluation of the design and functionality of user interfaces, involving actual visitors at every stage. We defined and built a suite of interactive and user-adaptive technologies for museum visitors, which was then evaluated at the Buonconsiglio Castle in Trento, Italy: (1) animated agents that help motivate visitors and focus their attention when necessary, (2) automatically generated, adaptive video documentaries on mobile devices, and (3) automatically generated post-visit summaries that reflect the individual interests of visitors as determined by their behavior and choices during their visit. These components are supported by underlying user modeling and inference mechanisms that allow for adaptivity and personalization. Novel software infrastructure allows for agent connectivity and fusion of multiple positioning data streams in the museum space. We conducted several experiments, focusing on various aspects of PEACH. In one, conducted with 110 visitors, we found evidence that even older users are comfortable interacting with a major component of the system.},
      Year = {2007} }



@article{
   Author = {Frias-Martinez, Enrique and Chen, Sherry and Macredie, Robert and Liu, Xiaohui},
   Title = {The role of Human Factors in Stereotyping Behavior and Perception of Digital Library Users: a Robust Clustering Approach},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {3},
   Pages = {305-337},
   Note = {DOI 10.1007/s11257-007-9028-7},
   Abstract = {To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception.},
      Year = {2007} }



@article{
   Author = {Miettinen, Miikka and Oulasvirta, Antti},
   Title = {Predicting Time-Sharing in Mobile Interaction},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {5},
   Pages = {475-510},
   Note = {DOI 10.1007/s11257-007-9033-x},
   Abstract = {The era of modern personal and ubiquitous computers is beset with the problem of fragmentation of the user's time between multiple tasks. Several adaptations have been envisioned that would support the performance of the user in the dynamically changing contexts in which interactions with mobile devices take place. This paper assesses the feasibility of sensor-based prediction of time-sharing, operationalized in terms of the number of glances, the duration of the longest glance, and the total and average durations of the glances to the interaction task. The data used for constructing and validating the predictive models was acquired from a field study (N=28), in which subjects performing mobile browsing tasks were observed for approximately 1 h in a variety of environments and situations. The predictive accuracy achieved in binary classification tasks was about 70% (about 20% above default), and the most informative sensors were related to the environment and interactions with the mobile device. Implications to the feasibility of different kinds of adaptations are discussed.},
   Keywords = {Time-sharing - Attention - Multitasking - Interruptions - Mobile interaction - Mobility - Classification - Predictive models - Bayesian networks},
   Year = {2007} }



@article{
   Author = {George, Sarah and Zukerman, Ingrid and Niemann, Michael},
   Title = {Inferences, Suppositions and Explanatory Extensions in Argument Interpretation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {17},
   Number = {5},
   Pages = {439-474},
   Note = {DOI 10.1007/s11257-007-9034-9},
   Abstract = {We describe a probabilistic approach for the interpretation of user arguments that integrates three aspects of an interpretation: inferences, suppositions and explanatory extensions. Inferences fill in information that connects the propositions in a user's argument, suppositions postulate new information that is likely believed by the user and is necessary to make sense of his or her argument, and explanatory extensions postulate information the user may have implicitly considered when constructing his or her argument. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation‰­Óa Bayesian network. Our evaluations show that suppositions and explanatory extensions are necessary components of interpretations, and that users consider appropriate the suppositions and explanatory extensions postulated by our system.},
      Year = {2007} }



@article{
   Author = {D'Mello, Sidney and Craig, Scotty and Witherspoon, Amy and McDaniel, Bethany and Graesser, Arthur},
   Title = {Automatic Detection of Learner's Affect from Conversational Cues},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {1},
   Pages = {45--80},
   Note = {DOI 10.1007/s11257-007-9037-6},
   Abstract = {Abstract~~We explored the reliability of detecting a learner‰€™s affect from conversational features extracted from interactions with AutoTutor, an intelligent tutoring system (ITS) that helps students learn by holding a conversation in natural language. Training data were collected in a learning session with AutoTutor, after which the affective states of the learner were rated by the learner, a peer, and two trained judges. Inter-rater reliability scores indicated that the classifications of the trained judges were more reliable than the novice judges. Seven data sets that temporally integrated the affective judgments with the dialogue features of each learner were constructed. The first four datasets corresponded to the judgments of the learner, a peer, and two trained judges, while the remaining three data sets combined judgments of two or more raters. Multiple regression analyses confirmed the hypothesis that dialogue features could significantly predict the affective states of boredom, confusion, flow, and frustration. Machine learning experiments indicated that standard classifiers were moderately successful in discriminating the affective states of boredom, confusion, flow, frustration, and neutral, yielding a peak accuracy of 42% with neutral (chance = 20%) and 54% without neutral (chance = 25%). Individual detections of boredom, confusion, flow, and frustration, when contrasted with neutral affect, had maximum accuracies of 69, 68, 71, and 78%, respectively (chance = 50%). The classifiers that operated on the emotion judgments of the trained judges and combined models outperformed those based on judgments of the novices (i.e., the self and peer). Follow-up classification analyses that assessed the degree to which machine-generated affect labels correlated with affect judgments provided by humans revealed that human-machine agreement was on par with novice judges (self and peer) but quantitatively lower than trained judges. We discuss the prospects of extending AutoTutor into an affect-sensing ITS. },
      Year = {2008} }



@article{
   Author = {Porayska-Pomsta, Ka?ka and Mavrikis, Manolis and Pain, Helen},
   Title = {Diagnosing and Acting on Student Affect: the Tutor's Perspective},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {1},
   Pages = {125-173},
   Note = {DOI 10.1007/s11257-007-9041-x},
   Abstract = {Abstract~~In this paper we explore human tutors‰€™inferences in relation to learners‰€™affective states and the relationship between those inferences and the actions that tutors take as their consequence. At the core of the investigations presented in this paper lie fundamental questions associated with the role of affective considerations in computer-mediated educational interactions. Theory of linguistic politeness is used as the basis for determining the contextual factors relevant to human tutors‰€™s actions, with special attention being dedicated to learner affective states. A study was designed to determine what affective states of the learners are relevant to tutoring mathematics and to identify the mechanisms used by tutors to predict such states. Logs of tutor-student dialogues were recorded along with contextual factors taken into consideration by tutors in relation to their specific tutorial dialogue moves. The logs were annotated in order to determine the types and range of student and tutor actions. Machine learning techniques were then applied to those actions to predict the values of three factors: student confidence, interest and effort. Whilst due to limited size and sparsity of data the results are not conclusive, they are very valuable as the basis for empirically derived hypotheses to be tested in further studies. The potential implications of the hypotheses, if they were confirmed by further studies, are discussed in relation to the impact of tutor‰€™s ability to diagnose student affect on the nature of computer-mediated tutorial interactions. },
      Year = {2008} }



@article{
   Author = {Yannakakis, Georgios and Hallam, John and Lund, Henrik},
   Title = {Entertainment Capture through Heart Rate Activity in Physical Interactive Playgrounds},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {1},
   Pages = {207--243},
   Note = {DOI 10.1007/s11257-007-9036-7},
   Abstract = {Abstract~~An approach for capturing and modeling individual entertainment (‰€œfun‰€) preferences is applied to users of the innovative Playware playground, an interactive physical playground inspired by computer games, in this study. The goal is to construct, using representative statistics computed from children‰€™s physiological signals, an estimator of the degree to which games provided by the playground engage the players. For this purpose children‰€™s heart rate (HR) signals, and their expressed preferences of how much ‰€œfun‰€ particular game variants are, are obtained from experiments using games implemented on the Playware playground. A comprehensive statistical analysis shows that children‰€™s reported entertainment preferences correlate well with specific features of the HR signal. Neuro-evolution techniques combined with feature set selection methods permit the construction of user models that predict reported entertainment preferences given HR features. These models are expressed as artificial neural networks and are demonstrated and evaluated on two Playware games and two control tasks requiring physical activity. The best network is able to correctly match expressed preferences in 64% of cases on previously unseen data (p‰ˆ’value 6 åį 10‰ˆ’5). The generality of the methodology, its limitations, its usability as a real-time feedback mechanism for entertainment augmentation and as a validation tool are discussed. },
      Year = {2008} }



@article{
cite-key,
   Author = {Carberry, Sandra and de Rosis, Fiorella},
   Title = {Introduction to special Issue on ‰€˜Affective modeling and adaptation‰€™},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {18},
   Number = {1},
   Pages = {1--9},
   Abstract = { We are all ruled in what we do by impulses; and these impulses are so organized that our actions in general serve for our self preservation and that of the race. Hunger, love, pain, fear are some of those inner forces which rule the individual‰€™s instinct for self preservation. At the same time, as social beings, we are moved in the relations with our fellow beings by such feelings as sympathy, pride, hate, need for power, pity and so on.   Albert Einstein, 1950   },
      Year = {2008} }



@article{
   Author = {McQuiggan, Scott and Mott, Bradford and Lester, James},
   Title = {Modeling Self-Efficacy in Intelligent Tutoring Systems: An Inductive Approach},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {1},
   Pages = {81-123},
   Note = {DOI 10.1007/s11257-007-9040-y},
   Abstract = {Abstract~~Self-efficacy is an individual‰€™s belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a student‰€™s level of self-efficacy. This article investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. It reports on two complementary empirical studies. In the first study, two families of self-efficacy models were induced: a static self-efficacy model, learned solely from pre-test (non-intrusively collected) data, and a dynamic self-efficacy model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. In the second empirical study, a similar experimental design was applied to an interactive narrative-centered learning environment. Self-efficacy models were induced from combinations of static and dynamic information, including pre-test data, physiological data, and observations of student behavior in the learning environment. The highest performing induced naĢųve Bayes models correctly classified 85.2% of instances in the first empirical study and 82.1% of instances in the second empirical study. The highest performing decision tree models correctly classified 86.9% of instances in the first study and 87.3% of instances in the second study. },
      Year = {2008} }



@article{
   Author = {Batliner, Anton and Steidl, Stefan and Hacker, Christian and Nšth, Elmar},
   Title = {Private Emotions versus Social Interaction: a Data-Driven Approach towards Analysing Emotion in Speech},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {1},
   Pages = {175-206},
   Note = {DOI 10.1007/s11257-007-9039-4},
   Abstract = {Abstract~~The ‰€˜traditional‰€™first two dimensions in emotion research are VALENCE and AROUSAL. Normally, they are obtained by using elicited, acted data. In this paper, we use realistic, spontaneous speech data from our ‰€˜AIBO‰€™ corpus (human-robot communication, children interacting with Sony‰€™s AIBO robot). The recordings were done in a Wizard-of-Oz scenario: the children believed that AIBO obeys their commands; in fact, AIBO followed a fixed script and often disobeyed. Five labellers annotated each word as belonging to one of eleven emotion-related states; seven of these states which occurred frequently enough are dealt with in this paper. The confusion matrices of these labels were used in a Non-Metrical Multi-dimensional Scaling to display two dimensions; the first we interpret as VALENCE, the second, however, not as AROUSAL but as INTERACTION, i.e., addressing oneself (angry, joyful) or the communication partner (motherese, reprimanding). We show that it depends on the specifity of the scenario and on the subjects‰€™ conceptualizations whether this new dimension can be observed, and discuss impacts on the practice of labelling and processing emotional data. Two-dimensional solutions based on acoustic and linguistic features that were used for automatic classification of these emotional states are interpreted along the same lines. },
      Year = {2008} }



@article{
   Author = {Forbes-Riley, Kate and Rotaru, Mihai and Litman, Diane},
   Title = {The Relative Impact of Student Affect on Performance Models in a spoken Dialogue Tutoring System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {1},
   Pages = {11--43},
   Note = {DOI 10.1007/s11257-007-9038-5},
   Abstract = {Abstract~~We hypothesize that student affect is a useful predictor of spoken dialogue system performance, relative to other parameters. We test this hypothesis in the context of our spoken dialogue tutoring system, where student learning is the primary performance metric. We first present our system and corpora, which have been annotated with several student affective states, student correctness and discourse structure. We then discuss unigram and bigram parameters derived from these annotations. The unigram parameters represent each annotation type individually, as well as system-generic features. The bigram parameters represent annotation combinations, including student state sequences and student states in the discourse structure context. We then use these parameters to build learning models. First, we build simple models based on correlations between each of our parameters and learning. Our results suggest that our affect parameters are among our most useful predictors of learning, particularly in specific discourse structure contexts. Next, we use the PARADISE framework (multiple linear regression) to build complex learning models containing only the most useful subset of parameters. Our approach is a value-added one; we perform a number of model-building experiments, both with and without including our affect parameters, and then compare the performance of the models on the training and the test sets. Our results show that when included as inputs, our affect parameters are selected as predictors in most models, and many of these models show high generalizability in testing. Our results also show that overall, the affect-included models significantly outperform the affect-excluded models. },
      Year = {2008} }



@article{
   Author = {Yudelson, Michael and Medvedeva, Olga and Crowley, Rebecca},
   Title = {A Multifactor Approach to Student Model Evaluation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {4},
   Pages = {349-382},
   Note = {DOI 10.1007/s11257-007-9046-5},
   Abstract = {Abstract~~Creating student models for Intelligent Tutoring Systems (ITS) in novel domains is often a difficult task. In this study, we outline a multifactor approach to evaluating models that we developed in order to select an appropriate student model for our medical ITS. The combination of areas under the receiver-operator and precision-recall curves, with residual analysis, proved to be a useful and valid method for model selection. We improved on Bayesian Knowledge Tracing with models that treat help differently from mistakes, model all attempts, differentiate skill classes, and model forgetting. We discuss both the methodology we used and the insights we derived regarding student modeling in this novel domain. },
      Year = {2008} }



@article{
   Author = {Damiano, Rossana and Gena, Cristina and Lombardo, Vincenzo and Nunnari, Fabrizio and Pizzo, Antonio},
   Title = {A Stroll with Carletto: Adaptation in Drama-based Tours with Virtual Characters},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {5},
   Note = {DOI 10.1007/s11257-008-9053-1},
   Abstract = {Abstract~~In this paper, we present an application for character-based guided tours on mobile devices. The application is based on the Dramatour methodology for information presentation, which incorporates a dramatic attitude in character-based presentations. The application has been developed for a historical site and is based on a virtual character, ‰€œCarletto‰€, a spider with an anthropomorphic aspect, who engages in a dramatized presentation of the site. Content items are delivered in a location-aware fashion, relying on a wireless network infrastructure, with visitors who can stroll freely. The selection of contents keeps track of user location and of the interaction history, in order to deliver the appropriate type and quantity of informative items, and to manage the given/new distinction in discourse. The communicative strategy of the character is designed to keep it believable along the interaction with the user, while enforcing dramatization effects. The design of the communicative strategy relies on the fact that the units of the presentation are tagged with metadata concerning their content and communicative function. The description of the application is accompanied by an evaluation study based on a sample of about 300 visitors, carried out in April 2006, when the installation was open to the public for 1 week. },
      Year = {2008} }



@article{
   Author = {Baker, Ryan and Corbett, Albert and Roll, Ido and Koedinger, Kenneth},
   Title = {Developing a Generalizable Detector of When Students Game the System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {3},
   Pages = {287-314},
   Note = {DOI 10.1007/s11257-007-9045-6},
   Abstract = {Abstract~~Some students, when working in interactive learning environments, attempt to ‰€œgame the system‰€, attempting to succeed in the environment by exploiting properties of the system rather than by learning the material and trying to use that knowledge to answer correctly. In this paper, we present a system that can accurately detect whether a student is gaming the system, within a Cognitive Tutor mathematics curricula. Our detector also distinguishes between two distinct types of gaming which are associated with different learning outcomes. We explore this detector‰€™s generalizability, and find that it transfers successfully to both new students and new tutor lessons. },
      Year = {2008} }



@article{
   Author = {Zimmermann, Andreas and Lorenz, Andreas},
   Title = {LISTEN: a User-Adaptive Audio-Augmented Museum Guide},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {5},
   Note = {DOI 10.1007/s11257-008-9049-x},
   Abstract = {Abstract~~Modern personalized information systems have been proven to support the user with information at the appropriate level and in the appropriate form. In specific environments like museums and exhibitions, focusing on the control of such a system is contradictory to establishing a relationship with the artifacts and exhibits. Preferably, the technology becomes invisible to the user and the physical reality becomes the interface to an additional virtual layer: by naturally moving in the space and/or manipulating physical objects in our surroundings the user will access information and operate the virtual layer. The LISTEN project is an attempt to make use of the inherent ‰€œeveryday‰€ integration of aural and visual perception, developing a tailored, immersive audio-augmented environment for the visitors of art exhibitions. The challenge of the LISTEN project is to provide a personalized immersive augmented environment, an aim which goes beyond the guiding purpose. The visitors of the museum implicitly interact with the system because the audio presentation is adapted to the users‰€™ contexts (e.g. interests, preferences, motion, etc.), providing an intelligent audio-based environment. This article describes the realization and user evaluation of the LISTEN system focusing on the personalization component. As this system has been installed at the Kunstmuseum Bonn in the context of an exhibition comprising artworks of the painter August Macke, a detailed evaluation could be conducted. },
      Year = {2008} }



@article{
cite-key,
   Author = {Berkovsky, Shlomo and Kuflik, Tsvi and Ricci, Francesco},
   Title = {Mediation of user models for enhanced personalization in recommender systems},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {18},
   Number = {3},
   Pages = {245--286},
   Note = {DOI 10.1007/s11257-007-9042-9},
   Abstract = {Abstract~~Provision of personalized recommendations to users requires accurate modeling of their interests and needs. This work proposes a general framework and specific methodologies for enhancing the accuracy of user modeling in recommender systems by importing and integrating data collected by other recommender systems. Such a process is defined as user models mediation. The work discusses the details of such a generic user modeling mediation framework. It provides a generic user modeling data representation model, demonstrates its compatibility with existing recommendation techniques, and discusses the general steps of the mediation. Specifically, four major types of mediation are presented: cross-user, cross-item, cross-context, and cross-representation. Finally, the work reports the application of the mediation framework and illustrates it with practical mediation scenarios. Evaluations of these scenarios demonstrate the potential benefits of user modeling data mediation, as in certain conditions it allows improving the quality of the recommendations provided to the users. },
      Year = {2008} }



@article{
   Author = {Sotiropoulos, Dionysios and Lampropoulos, Aristomenis and Tsihrintzis, George},
   Title = {MUSIPER: a System for Modeling Music Similarity Perception based on Objective Feature Subset Selection},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {4},
   Pages = {315-348},
   Note = {DOI 10.1007/s11257-007-9035-8},
   Abstract = {Abstract~~We explore the use of objective audio signal features to model the individualized (subjective) perception of similarity between music files. We present MUSIPER, a content-based music retrieval system which constructs music similarity perception models of its users by associating different music similarity measures to different users. Specifically, a user-supplied relevance feedback procedure and related neural network-based incremental learning allows the system to determine which subset of a set of objective features approximates more accurately the subjective music similarity perception of a specific user. Our implementation and evaluation of MUSIPER verifies the relation between subsets of objective features and individualized music similarity perception and exhibits significant improvement in individualized perceived similarity in subsequent music retrievals. },
      Year = {2008} }



@article{
   Author = {Carmagnola, Francesca and Cena, Federica and Console, Luca and Cortassa, Omar and Gena, Cristina and Goy, Anna and Torre, Ilaria and Toso, Andrea and Vernero, Fabiana},
   Title = {Tag-based User Modeling for Social Multi-Device Adaptive Guides},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {5},
   Note = {DOI 10.1007/s11257-008-9052-2},
   Abstract = {Abstract~~This paper aims to demonstrate that the principles of adaptation and user modeling, especially social annotation, can be integrated fruitfully with those of the web 2.0 paradigm and thereby enhance in the domain of cultural heritage. We propose a framework for improving recommender systems through exploiting the users tagging activity. We maintain that web 2.0‰€™s participative features can be exploited by adaptive web-based systems in order to enrich and extend the user model, improve social navigation and enrich information from a bottom-up perspective. Thus our approach stresses social annotation as a new and powerful kind of feedback and as a way to infer knowledge about users. The prototype implementation of our framework in the domain of cultural heritage is named iCITY. It is serving to demonstrate the validity of our approach and to highlight the benefits of this approach specifically for cultural heritage. iCITY is an adaptive, social, multi-device recommender guide that provides information about the cultural resources and events promoting the cultural heritage in the city of Torino. Our paper first describes this system and then discusses the results of a set of evaluations that were carried out at different stages of the systems development and aimed at validating the framework and implementation of this specific prototype. In particular, we carried out a heuristic evaluation and two sets of usability tests, aimed at checking the usability of the user interface, specifically of the adaptive behavior of the system. Moreover, we conducted evaluations aimed at investigating the role of tags in the definition of the user model and the impact of tags on the accuracy of recommendations. Our results are encouraging. },
      Year = {2008} }



@article{
   Author = {Cramer, Henriette and Evers, Vanessa and Ramlal, Satyan and van Someren, Maarten and Rutledge, Lloyd and Stash, Natalia and Aroyo, Lora and Wielinga, Bob},
   Title = {The Effects of Transparency on Trust in and Acceptance of a Content-based Art Recommender},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {18},
   Number = {5},
   Note = {DOI 10.1007/s11257-008-9051-3},
   Abstract = {Abstract~~The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study‰€™s results. },
      Year = {2008} }



@article{
p00764,
   Author = {Torre, I.},
   Title = {Adaptive systems in the era of the semantic and social web, a survey},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {5},
   Pages = {433-486},
   Note = {DOI 10.1007/s11257-009-9067-3},
   Abstract = {Abstract  In this paper we provide a classification of adaptive systems with respect to the kind of semantic technology they exploit to accomplish or improve specific adaptation and user modeling tasks. This classification is based on a distinction between strong semantic techniques and weak semantic techniques. The former are techniques based on the Semantic Web, while the latter regard technologies that, in different ways, annotate resources, enriching their meaning. This second category includes, in particular, Web 2.0 social annotations and mixed approaches between social annotations and Semantic Web techniques. While the impact of the Semantic Web on adaptive systems has been discussed in several survey papers, the potential of weak semantic technologies has, so far, received little attention. The aim of this analysis is to fill this gap. Therefore, we will discuss contributions and limits of both approaches, but we will focus special attention on weak semantic adaptive systems.},
      Year = {2009} }



@article{
p00760,
   Author = {de Campos, L. and Fern‡ndez-Luna, J. and Huete, J. and Rueda-Morales, M.},
   Title = {Managing uncertainty in group recommending processes},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {3},
   Pages = {207-242},
   Note = {DOI 10.1007/s11257-008-9061-1},
   Abstract = {Abstract  While the problem of building recommender systems has attracted considerable attention in recent years, most recommender systems are designed for recommending items to individuals. The aim of this paper is to automatically recommend a ranked list of new items to a group of users. We will investigate the value of using Bayesian networks to represent the different uncertainties involved in a group recommending process, i.e. those uncertainties related to mechanisms that govern the interactions between group members and the processes leading to the final choice or recommendation. We will also show how the most common aggregation strategies might be encoded using a Bayesian network formalism. The proposed model can be considered as a collaborative Bayesian network-based group recommender system, where group ratings are computed from the past voting patterns of other users with similar tastes.},
      Year = {2009} }



@article{
p00754,
   Author = {Garc’a, E. and Romero, C. and Ventura, S. and Castro, C.},
   Title = {An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {1},
   Pages = {99-132},
   Note = {DOI 10.1007/s11257-008-9047-z},
   Abstract = {Abstract  Nowadays we find more and more applications for data mining techniques in e-learning and web-based adaptive educational systems. The useful information discovered can be used directly by the teacher or author of the course in order to improve instructional/learning performance. This can, however, imply a lot of work for the teacher who can greatly benefit from the help of educational recommender systems for doing this task. In this paper we propose a system oriented to find, share and suggest the most appropriate modifications to improve the effectiveness of the course. We describe an iterative methodology to develop and carry out the maintenance of web-based courses to which we have added a specific data mining step. We apply association rule mining to discover interesting information through studentsÕ usage data in the form of IF-THEN recommendation rules. We have also used a collaborative recommender system to share and score the recommendation rules obtained by teachers with similar profiles along with other experts in education. Finally, we have carried out experiments with several real groups of students using a web-based adaptive course. The results obtained demonstrate that the proposed architecture constitutes a good starting point to future investigations in order to generalize the results over many course contents.},
      Year = {2009} }



@article{
p00763,
   Author = {Walker, E. and Rummel, N. and Koedinger, K.},
   Title = {CTRL: A research framework for providing adaptive collaborative learning support},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {5},
   Pages = {387-431},
   Note = {DOI 10.1007/s11257-009-9069-1},
   Abstract = {Abstract  There is evidence suggesting that providing adaptive assistance to collaborative interactions might be a good way of improving the effectiveness of collaborative activities. In this paper, we introduce the Collaborative Tutoring Research Lab (CTRL), a research-oriented framework for adaptive collaborative learning support that enables researchers to combine different types of adaptive support, particularly by using domain-specific models as input to domain-general components in order to create more complex tutoring functionality. Additionally, the framework allows researchers to implement comparison conditions by making it easier to vary single factors of the adaptive intervention. We evaluated CTRL by designing adaptive and fixed support for a peer tutoring setting, and instantiating the framework using those two collaborative scenarios and an individual tutoring scenario. As part of the implementation, we integrated pre-existing components from the Cognitive Tutor Algebra (CTA) with custom-built components. The three conditions were then compared in a controlled classroom study, and the results helped us to contribute to learning sciences research in peer tutoring. CTRL can be generalized to other collaborative scenarios, but the ease of implementation relates to the complexity of the existing components used. CTRL as a framework has yielded a full implementation of an adaptive support system and a controlled evaluation in the classroom.},
      Year = {2009} }



@article{
p00752,
   Author = {Berkovsky, S. and Kuflik, T. and Ricci, F.},
   Title = {Cross-representation mediation of user models},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {1},
   Pages = {35-63},
   Note = {DOI 10.1007/s11257-008-9055-z},
   Abstract = {Abstract  Personalization is considered a powerful methodology for improving the effectiveness of information search and decision making. It has led to the dissemination of systems capable of suggesting relevant and personalized information (or items) to the users, according to their characteristics and preferences, as represented by a User Model (UM). Since the quality of the personalization largely depends on the size and accuracy of the managed UMs, it would be beneficial to enrich the UMs by mediating, i.e., importing and integrating, UMs built by other personalization systems. This work discusses and evaluates a cross-representation mediation of UMs from collaborative filtering to content-based recommender systems. According to this approach, a content-based recommender system, having partial or no UM data, can generate recommendations for users by mediating UM data of the same users, collected by a collaborative filtering system. The mediation process transforms the UMs from the collaborative filtering ratings to the content-based weighted item features. The mediation process exploits the item descriptions that are typically not used by the collaborative filtering recommender systems. An experimental evaluation conducted in the domain of movies shows that for users with small collaborative filtering UMs, i.e., users with few item ratings, the accuracy of the recommendations provided using the mediated content-based UMs is superior to that using the original collaborative filtering UMs. Moreover, it shows that the mediation can be used to improve a content-based recommender system by incrementally mediating collaborative filtering UM data (item ratings) and enriching the available content-based UMs.},
      Year = {2009} }



@article{
p00756,
   Author = {Conati, C. and Maclaren, H.},
   Title = {Empirically building and evaluating a probabilistic model of user affect},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {3},
   Pages = {267-303},
   Note = {DOI 10.1007/s11257-009-9062-8},
   Abstract = {Abstract  We present a probabilistic model of user affect designed to allow an intelligent agent to recognise multiple user emotions during the interaction with an educational computer game. Our model is based on a probabilistic framework that deals with the high level of uncertainty involved in recognizing a variety of user emotions by combining in a Dynamic Bayesian Network information on both the causes and effects of emotional reactions. The part of the framework that reasons from causes to emotions (diagnostic model) implements a theoretical model of affect, the OCC model, which accounts for how emotions are caused by oneÕs appraisal of the current context in terms of oneÕs goals and preferences. The advantage of using the OCC model is that it provides an affective agent with explicit information not only on which emotions a user feels but also why, thus increasing the agentÕs capability to effectively respond to the usersÕ emotions. The challenge is that building the model requires having mechanisms to assess user goals and how the environment fits them, a form of plan recognition. In this paper, we illustrate how we built the predictive part of the affective model by combining general theories with empirical studies to adapt the theories to our target application domain. We then present results on the modelÕs accuracy, showing that the model achieves good accuracy on several of the target emotions. We also discuss the modelÕs limitations, to open the ground for the next stage of the work, i.e., complementing the model with diagnostic information.},
      Year = {2009} }



@article{
p00750,
   Author = {Mobasher, B. and Tuzhilin, A.},
   Title = {Preface to the special issue on data mining for personalization},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {1},
   Pages = {1-3},
   Note = {DOI 10.1007/s11257-008-9060-2},
      Year = {2009} }



@article{
p00762,
   Author = {Ajanki, A. and Hardoon, D. and Kaski, S. and PuolamŠki, K. and Shawe-Taylor, J.},
   Title = {Can eyes reveal interest? Implicit queries from gaze patterns},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {4},
   Pages = {307-339},
   Note = {DOI 10.1007/s11257-009-9066-4},
   Abstract = {Abstract  We study a new research problem, where an implicit information retrieval query is inferred from eye movements measured when the user is reading, and used to retrieve new documents. In the training phase, the userÕs interest is known, and we learn a mapping from how the user looks at a term to the role of the term in the implicit query. Assuming the mapping is universal, that is, the same for all queries in a given domain, we can use it to construct queries even for new topics for which no learning data is available. We constructed a controlled experimental setting to show that when the system has no prior information as to what the user is searching, the eye movements help significantly in the search. This is the case in a proactive search, for instance, where the system monitors the reading behaviour of the user in a new topic. In contrast, during a search or reading session where the set of inspected documents is biased towards being relevant, a stronger strategy is to search for content-wise similar documents than to use the eye movements.},
      Year = {2009} }



@article{
p00751,
   Author = {Mehta, B. and Nejdl, W.},
   Title = {Unsupervised strategies for shilling detection and robust collaborative filtering},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {1},
   Pages = {65-97},
   Note = {DOI 10.1007/s11257-008-9050-4},
   Abstract = {Abstract  Collaborative filtering systems are essentially social systems which base their recommendation on the judgment of a large number of people. However, like other social systems, they are also vulnerable to manipulation by malicious social elements. Lies and Propaganda may be spread by a malicious user who may have an interest in promoting an item, or downplaying the popularity of another one. By doing this systematically, with either multiple identities, or by involving more people, malicious user votes and profiles can be injected into a collaborative recommender system. This can significantly affect the robustness of a system or algorithm, as has been studied in previous work. While current detection algorithms are able to use certain characteristics of shilling profiles to detect them, they suffer from low precision, and require a large amount of training data. In this work, we provide an in-depth analysis of shilling profiles and describe new approaches to detect malicious collaborative filtering profiles. In particular, we exploit the similarity structure in shilling user profiles to separate them from normal user profiles using unsupervised dimensionality reduction. We present two detection algorithms; one based on PCA, while the other uses PLSA. Experimental results show a much improved detection precision over existing methods without the usage of additional training time required for supervised approaches. Finally, we present a novel and highly effective robust collaborative filtering algorithm which uses ideas presented in the detection algorithms using principal component analysis.},
      Year = {2009} }



@article{
p00753,
   Author = {Zanker, M. and Jessenitschnig, M.},
   Title = {Case-studies on exploiting explicit customer requirements in recommender systems},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {1},
   Pages = {133-166},
   Note = {DOI 10.1007/s11257-008-9048-y},
   Abstract = {Abstract  Recommender Systems (RS) suggest useful and interesting items to users in order to increase user satisfaction and online conversion rates. They typically exploit explicit or implicit user feedback such as ratings, buying records or clickstream data and apply statistical methods to derive recommendations. This paper focuses on explicitly formulated customer requirements as the sole type of user feedback. Its contribution lies in comparing different techniques such as knowledge- and utility-based methods, collaborative filtering, association rule mining as well as hybrid variants when user models consist solely of explicit customer requirements. We examine how this type of user feedback can be exploited for personalization in e-commerce scenarios. Furthermore, examples of actual online shops are developed where such contextual user information is available, demonstrating how more efficient RS configurations can be implemented. Results indicate that, especially for new users, explicit customer requirements are a useful source of feedback for personalization and hybrid configurations of collaborative and knowledge-based techniques achieve best results.},
      Year = {2009} }



@article{
p00761,
   Author = {Cocea, M. and Weibelzahl, S.},
   Title = {Log file analysis for disengagement detection in e-Learning environments},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {4},
   Pages = {341-385},
   Note = {DOI 10.1007/s11257-009-9065-5},
   Abstract = {Abstract  Most e-Learning systems store data about the learnerÕs actions in log files, which give us detailed information about learner behaviour. Data mining and machine learning techniques can give meaning to these data and provide valuable information for learning improvement. One area that is of particular importance in the design of e-Learning systems is learner motivation as it is a key factor in the quality of learning and in the prevention of attrition. One aspect of motivation is engagement, a necessary condition for effective learning. Using data mining techniques for log file analysis, our research investigates the possibility of predicting usersÕ level of engagement, with a focus on disengaged learners. As demonstrated previously across two different e-Learning systems, HTML-Tutor and iHelp, disengagement can be predicted by monitoring the learnersÕ actions (e.g. reading pages and taking test/quizzes). In this paper we present the findings of three studies that refine this prediction approach. Results from the first study show that two additional reading speed attributes can increase the accuracy of prediction. The second study suggests that distinguishing between two different patterns of disengagement (spending a long time on a page/test and browsing quickly through pages/tests) may improve prediction in some cases. The third study demonstrates the influence of exploratory behaviour on prediction, as most users at the first login familiarize themselves with the system before starting to learn.},
      Year = {2009} }



@article{
p00758,
   Author = {Chen, L. and Pu, P.},
   Title = {Interaction design guidelines on critiquing-based recommender systems},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {3},
   Pages = {167-206},
   Note = {DOI 10.1007/s11257-008-9057-x},
   Abstract = {Abstract  A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the userÕs preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the userÕs interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing-based recommender system: critiquing coverageŃone vs. multiple items that are returned during each recommendation cycle to be critiqued; and critiquing aidŃsystem-suggested critiques (i.e., a set of critique suggestions for users to select) vs. user-initiated critiquing facility (i.e., facilitating users to create critiques on their own). Through a series of three user trials, we have measured how real-users reacted to systems with varied setups of the two elements. In particular, it was found that giving users the choice of critiquing one of multiple items (as opposed to just one) has significantly positive impacts on increasing usersÕ decision accuracy (particularly in the first recommendation cycle) and saving their objective effort (in the later critiquing cycles). As for critiquing aids, the hybrid design with both system-suggested critiques and user-initiated critiquing support exhibits the best performance in inspiring usersÕ decision confidence and increasing their intention to return, in comparison with the uncombined exclusive approaches. Therefore, the results from our studies shed light on the design guidelines for determining the sweetspot balancing user initiative and system support in the development of an effective and user-centric critiquing-based recommender system.},
      Year = {2009} }



@article{
p00757,
   Author = {Feng, M. and Heffernan, N. and Koedinger, K.},
   Title = {Addressing the assessment challenge with an online system that tutors as it assesses},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {3},
   Pages = {243-266},
   Note = {DOI 10.1007/s11257-009-9063-7},
   Abstract = {Abstract  Secondary teachers across the United States are being asked to use formative assessment data (Black and Wiliam 1998a,b; Roediger and Karpicke 2006) to inform their classroom instruction. At the same time, critics of US governmentÕs No Child Left Behind legislation are calling the bill ŅNo Child Left UntestedÓ. Among other things, critics point out that every hour spent assessing students is an hour lost from instruction. But, does it have to be? What if we better integrated assessment into classroom instruction and allowed students to learn during the test? We developed an approach that provides immediate tutoring on practice assessment items that students cannot solve on their own. Our hypothesis is that we can achieve more accurate assessment by not only using data on whether students get test items right or wrong, but by also using data on the effort required for students to solve a test item with instructional assistance. We have integrated assistance and assessment in the ASSISTment system. The system helps teachers make better use of their time by offering instruction to students while providing a more detailed evaluation of student abilities to the teachers, which is impossible under current approaches. Our approach for assessing student math proficiency is to use data that our system collects through its interactions with students to estimate their performance on an end-of-year high stakes state test. Our results show that we can do a reliably better job predicting student end-of-year exam scores by leveraging the interaction data, and the model based on only the interaction information makes better predictions than the traditional assessment model that uses only information about correctness on the test items.},
      Year = {2009} }



@article{
p00755,
   Author = {Stamou, S. and Ntoulas, A.},
   Title = {Search personalization through query and page topical analysis},
   Journal = {User Modeling and User-Adapted Interaction},
   Volume = {19},
   Number = {1},
   Pages = {5-33},
   Note = {DOI 10.1007/s11257-008-9056-y},
   Abstract = {Abstract  Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a userÕs topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the userÕs current query and the query-matching pages, in order to identify the userÕs current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.},
      Year = {2009} }


@article{
   Author = {Niu, William T. and Kay, Judy},
   Title = {PERSONAF: Framework for Personalised Ontological Reasoning in Pervasive Computing},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {20},
   Number = {1},
   Pages = {1-40},
   Note = {DOI 10.1007/s11257-009-9068-2},
   Abstract = {Pervasive computing creates possibilities for presenting highly personalised information about the people, places and things in a building. One of the challenges for such personalisation is the creation of the system that can support ontological reasoning for several key tasks: reasoning about location; personalisation of information about location at the right level of detail; and personalisation to match each personÕs conceptions of the building based on their own use of it and their relationship to other people in the building. From pragmatic perspectives, it should be inexpensive to create the ontology for each new building. It is also critical that users should be able to understand and control pervasive applications. We created the PERSONAF (personalised pervasive scrutable ontological framework) to address these challenges. PERSONAF is a new abstract framework for pervasive ontological reasoning. We report its evaluation at three levels. First, we assessed the power of the ontology for reasoning about noisy and uncertain location information, showing that PERSONAF can improve location modelling. Notably, the best ontological reasoner varies across users. Second, we demonstrate the use of the PERSONAF framework in Adaptive Locator, an application built upon it, using our low cost mechanisms for non-generic layers of the ontology. Finally, we report a user study, which evaluated the PERSONAF approach as seen by users in the Adaptive Locator. We assessed both the personalisation performance and the understandability of explanations of the system reasoning. Together, these three evaluations show that the PERSONAF approach supports building of low cost ontologies, that can achieve flexible ontological reasoning about smart buildings and the people in them, and that this can be used to build applications which give personalised information that can provide understandable explanations of the reasoning underlying the personalisation.},
      Year = {2010} }



@article{
   Author = {De Meo, Pasquale and Quattrone, Giovanni and Ursino, Domenico},
   Title = {A Query Expansion and User Profile Enrichment Approach to Improve the Performance of Recommender Systems Operating on a Folksonomy},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {20},
   Number = {1},
   Pages = {41-86},
   Note = {DOI 10.1007/s11257-010-9072-6},
   Abstract = {In this paper we propose a query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy, storing and classifying the tags used to label a set of available resources. Our approach builds and maintains a profile for each user. When he submits a query (consisting of a set of tags) on this folksonomy to retrieve a set of resources of his interest, it automatically finds further  authoritative  tags to enrich his query and proposes them to him. All  authoritative  tags considered interesting by the user are exploited to refine his query and, along with those tags directly specified by him, are stored in his profile in such a way to enrich it. The expansion of user queries and the enrichment of user profiles allow any content-based recommender system operating on the folksonomy to retrieve and suggest a high number of resources matching with user needs and desires. Moreover, enriched user profiles can guide any collaborative filtering recommender system to proactively discover and suggest to a user many resources relevant to him, even if he has not explicitly searched for them.},
   Keywords = {Computer Science},
   Year = {2010} }



@article{
   Author = {Recabarren, Mat’as and Nussbaum, Miguel},
   Title = {Exploring the Feasibility of Web Form Adaptation to UsersÕ Cultural Dimension Scores},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {20},
   Number = {1},
   Pages = {87-108},
   Note = {DOI 10.1007/s11257-010-9071-7},
   Abstract = {With many daily tasks now performed on the Internet, productivity and efficiency in working with web pages have become transversal necessities for all users. Many of these tasks involve the inputting of user information, obligating the user to interact with a webform. Research has demonstrated that productivity depends largely on usersÕ personal characteristics, implying that it will vary from user to user. The webform development process must therefore, include modeling of its intended users to ensure the interface design is appropriate. Taking all potential users into account is difficult, however, primarily because their identity is unknown, and some may be effectively excluded by the final design. Such discrimination can be avoided by incorporating rules that allow webforms to adapt automatically to the individual userÕs characteristics, the principal one being the personÕs culture. In this paper we report two studies that validate this option. We begin by determining the relationships between a userÕs cultural dimension scores and their behavior when faced with a webform. We then validate the notion that rules based on these relationships can be established for the automatic adaptation of a webform in order to reduce the time taken to complete it. We conclude that the automatic webform adaptation to the cultural dimensions of users improves their performance.},
      Year = {2010} }



@article{
   Author = {Ghazarian, Arin and Noorhosseini, S. Majid},
   Title = {Automatic detection of usersÕ skill levels using high-frequency user interface events},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {20},
   Number = {2},
   Pages = {109-146},
   Note = {DOI 10.1007/s11257-010-9073-5},
   Abstract = {Computer users have different levels of system skills. Moreover, each user has different levels of skill across different applications and even in different portions of the same application. Additionally, usersÕ skill levels change dynamically as users gain more experience in a user interface. In order to adapt user interfaces to the different needs of user groups with different levels of skills, automatic methods of skill detection are required. In this paper, we present our experiments and methods, which are used to build automatic skill classifiers for desktop applications. Machine learning algorithms were used to build statistical predictive models of skill. Attribute values were extracted from high frequency user interface events, such as mouse motions and menu interactions, and were used as inputs to our models. We have built both task-independent and task-dependent classifiers with promising results.},
   Keywords = {Computer Science},
   Year = {2010} }



@article{
   Author = {DÕMello, Sidney K. and Graesser, Arthur},
   Title = {Multimodal Semi-Automated Affect Detection from Conversational Cues, Gross Body Language, and Facial Features},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {20},
   Number = {2},
   Pages = {147-187},
   Note = {DOI 10.1007/s11257-010-9074-4},
   Abstract = {We developed and evaluated a multimodal affect detector that combines conversational cues, gross body language, and facial features. The multimodal affect detector uses feature-level fusion to combine the sensory channels and linear discriminant analyses to discriminate between naturally occurring experiences of boredom, engagement/flow, confusion, frustration, delight, and neutral. Training and validation data for the affect detector were collected in a study where 28 learners completed a 32-Źmin. tutorial session with AutoTutor, an intelligent tutoring system with conversational dialogue. Classification results supported a channel ? judgment type interaction, where the face was the most diagnostic channel for spontaneous affect judgments (i.e., at any time in the tutorial session), while conversational cues were superior for fixed judgments (i.e., every 20 s in the session). The analyses also indicated that the accuracy of the multichannel model (face, dialogue, and posture) was statistically higher than the best single-channel model for the fixed but not spontaneous affect expressions. However, multichannel models reduced the discrepancy (i.e., variance in the precision of the different emotions) of the discriminant models for both judgment types. The results also indicated that the combination of channels yielded superadditive effects for some affective states, but additive, redundant, and inhibitory effects for others. We explore the structure of the multimodal linear discriminant models and discuss the implications of some of our major findings.},
      Year = {2010} }



@article{
   Author = {Mairesse, Franois and Walker, Marilyn A.},
   Title = {Towards Personality-based User Adaptation: Psychologically Informed Stylistic Language Generation},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {20},
   Number = {3},
   Pages = {227-278},
   Note = {DOI 10.1007/s11257-010-9076-2},
   Abstract = {Conversation is an essential component of social behavior, one of the primary means by which humans express intentions, beliefs, emotions, attitudes and personality. Thus the development of systems to support natural conversational interaction has been a long term research goal. In natural conversation, humans adapt to one another across many levels of utterance production via processes variously described as linguistic style matching, entrainment, alignment, audience design, and accommodation. A number of recent studies strongly suggest that dialogue systems that adapted to the user in a similar way would be more effective. However, a major research challenge in this area is the ability to dynamically generate user-adaptive utterance variations. As part of a personality-based user adaptation framework, this article describes personage, a highly parameterizable generator which provides a large number of parameters to support adaptation to a userÕs linguistic style. We show how we can systematically apply results from psycholinguistic studies that document the linguistic reflexes of personality, in order to develop models to control personageÕs parameters, and produce utterances matching particular personality profiles. When we evaluate these outputs with human judges, the results indicate that humans perceive the personality of system utterances in the way that the system intended.},
      Year = {2010} }



@article{
   Author = {Tkal?i?, Marko and Burnik, Urban and Ko?ir, Andrej},
   Title = {Using Affective Parameters in a Content-based Recommender System for Images},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {20},
   Number = {4},
   Pages = {279-311},
   Note = {DOI 10.1007/s11257-010-9079-z},
   Abstract = {There is an increasing amount of multimedia content available to end users. Recommender systems help these end users by selecting a small but relevant subset of items for each user based on her/his preferences. This paper investigates the influence of affective metadata (metadata that describe the userÕs emotions) on the performance of a content-based recommender (CBR) system for images. The underlying assumption is that affective parameters are more closely related to the userÕs experience than generic metadata (e.g. genre) and are thus more suitable for separating the relevant items from the non-relevant. We propose a novel affective modeling approach based on usersÕ emotive responses. We performed a user-interaction session and compared the performance of the recommender system with affective versus generic metadata. The results of the statistical analysis showed that the proposed affective parameters yield a significant improvement in the performance of the recommender system.},
      Year = {2010} }



@article{
   Author = {Yannakakis, Georgios N. and Mart’nez, HŽctor P. and Jhala, Arnav},
   Title = {Towards Affective Camera Control in Games},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {20},
   Number = {4},
   Pages = {313-340},
   Note = {DOI 10.1007/s11257-010-9078-0},
   Abstract = {Information about interactive virtual environments, such as games, is perceived by users through a virtual camera. While most interactive applications let users control the camera, in complex navigation tasks within 3D environments users often get frustrated with the interaction. In this paper, we propose inclusion of camera control as a vital component of affective adaptive interaction in games. We investigate the impact of camera viewpoints on psychophysiology of players through preference surveys collected from a test game. Data is collected from players of a 3D prey/predator game in which player experience is directly linked to camera settings. Computational models of discrete affective states of fun, challenge, boredom, frustration, excitement, anxiety and relaxation are built on biosignal (heart rate, blood volume pulse and skin conductance) features to predict the pairwise self-reported emotional preferences of the players. For this purpose, automatic feature selection and neuro-evolutionary preference learning are combined providing highly accurate affective models. The performance of the artificial neural network models on unseen data reveals accuracies of above 80% for the majority of discrete affective states examined. The generality of the obtained models is tested in different test-bed game environments and the use of the generated models for creating adaptive affect-driven camera control in games is discussed.},
      Year = {2010} }



@article{
   Author = {Foster, Mary Ellen and Oberlander, Jon},
   Title = {User Preferences Can Drive Facial Expressions: Evaluating an Embodied Conversational Agent in a Recommender Dialogue System},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {20},
   Number = {4},
   Pages = {341-381},
   Note = {DOI 10.1007/s11257-010-9080-6},
   Abstract = {Tailoring the linguistic content of automatically generated descriptions to the preferences of a target user has been well demonstrated to be an effective way to produce higher-quality output that may even have a greater impact on user behaviour. It is known that the non-verbal behaviour of an embodied agent can have a significant effect on usersÕ responses to content presented by that agent. However, to date no-one has examined the contribution of non-verbal behaviour to the effectiveness of user tailoring in automatically generated embodied output. We describe a series of experiments designed to address this question. We begin by introducing a multimodal dialogue system designed to generate descriptions and comparisons tailored to user preferences, and demonstrate that the user-preference tailoring is detectable to an overhearer when the output is presented as synthesised speech. We then present a multimodal corpus consisting of the annotated facial expressions used by a speaker to accompany the generated tailored descriptions, and verify that the most characteristic positive and negative expressions used by that speaker are identifiable when resynthesised on an artificial talking head. Finally, we combine the corpus-derived facial displays with the tailored descriptions to test whether the addition of the non-verbal channel improves usersÕ ability to detect the intended tailoring, comparing two strategies for selecting the displays: one based on a simple corpus-derived rule, and one making direct use of the full corpus data. The performance of the subjects who saw displays selected by the rule-based strategy was not significantly different than that of the subjects who got only the linguistic content, while the subjects who saw the data-driven displays were significantly worse at detecting the correctly tailored output. We propose a possible explanation for this result, and also make recommendations for developers of future systems that may make use of an embodied agent to present user-tailored content.},
      Year = {2010} }



@article{
   Author = {Bousbia, Nabila and Reba•, Issam and Labat, Jean-Marc and Balla, Amar},
   Title = {LearnersÕ Navigation Behavior Identification Based on Trace Analysis},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {20},
   Number = {5},
   Note = {DOI 10.1007/s11257-010-9081-5},
   Abstract = {Identifying learnersÕ behaviors and learning preferences or styles in a Web-based learning environment is crucial for organizing the tracking and specifying how and when assistance is needed. Moreover, it helps online course designers to adapt the learning material in a way that guarantees individualized learning, and helps learners to acquire meta-cognitive knowledge. The goal of this research is to identify learnersÕ behaviors and learning styles automatically during training sessions, based on trace analysis. In this paper, we focus on the identification of learnersÕ behaviors through our system: Indicators for the Deduction of Learning Styles. We shall first present our trace analysis approach. Then, we shall propose a Ōnavigation typeÕ indicator to analyze learnersÕ behaviors and we shall define a method for calculating it. To this end, we shall build a decision tree based on semantic assumptions and tests. To validate our approach, and improve the proposed calculation method, we shall present and discuss the results of two experiments that we conducted.},
      Year = {2010} }



@article{
   Author = {Loboda, Tomasz D. and Brusilovsky, Peter},
   Title = {User-Adaptive Explanatory Program Visualization: Evaluation and Insights from Eye Movements},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {20},
   Number = {3},
   Pages = {191-226},
   Note = {DOI 10.1007/s11257-010-9077-1},
   Abstract = {User-adaptive visualization and explanatory visualization have been suggested to increase educational effectiveness of program visualization. This paper presents an attempt to assess the value of these two approaches. The results of a controlled experiment indicate that explanatory visualization allows students to substantially increase the understanding of a new programming topic. Furthermore, an educational application that features explanatory visualization and employs a user model to track usersÕ progress allows students to interact with a larger amount of material than an application which does not follow usersÕ activity. However, no support for the difference in short-term knowledge gain between the two applications is found. Nevertheless, students admit that they prefer the version that estimates and visualizes their progress and adapts the learning content to their level of understanding. They also use the applicationÕs estimation to pace their work. The differences in eye movement patterns between the applications employing adaptive and non-adaptive explanatory visualizations are investigated as well. Gaze-based measures show that adaptive visualization captivates attention more than its non-personalized counterpart and is more interesting to students. Natural language explanations also accumulate a big portion of studentsÕ attention. Furthermore, the results indicate that working memory span can mediate the perception of adaptation. It is possible that user-adaptation in an educational context provides a different service to people with different mental processing capabilities.},
      Year = {2010} }



@article{
   Author = {Paramythis, Alexandros and Weibelzahl, Stephan and Masthoff, Judith},
   Title = {Layered Evaluation of Interactive Adaptive Systems: Framework and Formative Methods},
   Journal = {User Modeling and User-Adapted Interaction: The Journal of Personalization Research},
   Volume = {20},
   Number = {5},
   Note = {http://www.umuai.org/Paramythis-etal-UMUAI-2010.pdf},
   Abstract = {User-adaptive visualization and explanatory visualization have been suggested to increase educational effectiveness of program visualization. This paper presents an attempt to assess the value of these two approaches. The results of a controlled experiment indicate that explanatory visualization allows students to substantially increase the understanding of a new programming topic. Furthermore, an educational application that features explanatory visualization and employs a user model to track usersÕ progress allows students to interact with a larger amount of material than an application which does not follow usersÕ activity. However, no support for the difference in short-term knowledge gain between the two applications is found. Nevertheless, students admit that they prefer the version that estimates and visualizes their progress and adapts the learning content to their level of understanding. They also use the applicationÕs estimation to pace their work. The differences in eye movement patterns between the applications employing adaptive and non-adaptive explanatory visualizations are investigated as well. Gaze-based measures show that adaptive visualization captivates attention more than its non-personalized counterpart and is more interesting to students. Natural language explanations also accumulate a big portion of studentsÕ attention. Furthermore, the results indicate that working memory span can mediate the perception of adaptation. It is possible that user-adaptation in an educational context provides a different service to people with different mental processing capabilities.},
      Year = {2010} }




<script type="text/javascript">(function (d, w) {var x = d.getElementsByTagName('SCRIPT')[0];var f = function () {var s = d.createElement('SCRIPT');s.type = 'text/javascript';s.async = true;s.src = "//np.lexity.com/embed/YW/ee42eade4b9db643f5ef575b8cf1d67e?id=97bdbc379ad6";x.parentNode.insertBefore(s, x);};w.attachEvent ? w.attachEvent('onload',f) :w.addEventListener('load',f,false);}(document, window));</script>