Alphabetical List of Works


Akoulchina, I., & Ganascia, J. (1997). SATELIT-Agent: An adaptive interface based on learning interface agents technology. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 21-32). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Help U to Find Information Recommend Web pages
Other Functions Verify U's competence to add information to S
Properties Proficiencies U's domain expertise and theoretical orientation
Current Goal Goals of U's Web searches
Input Types Responses to Test or Practice Items Answers to test questions
Naturally Occurring Actions Hypermedia pages that U has visited
Inference Application-Specific Computations Procedure for assessing U's domain expertise on the basis of U's answers to test questions


Albrecht, W., David, Zukerman, I., Nicholson, A. E., & Bud, A. (1997). Towards a Bayesian model for keyhole plan recognition in large domains. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 365-376). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Other Functions Predict goals, actions, and locations of an agent in a large domain
Input Types Naturally Occurring Actions U's actions and locations within a large domain
Inference Decision-Theoretic Methods Dynamic Bayesian Networks for prediction of goals, actions, and locations
Empirical Foundations Learning Parameters for Users in General Derivation of conditional probability distributions for a Bayesian network from a database of observations


Ambrosini, L., Cirillo, V., & Micarelli, A. (1997). A hybrid architecture for user-adapted information filtering on the World Wide Web. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 59-61). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Help U to Find Information Filter WWW documents in accordance with U's interests
Properties General Interests and Preferences U's interests with respect to information on the WWW
Inference Neural Networks and Related Techniques Neural networks as an alternative technique for triggering stereotypes
Stereotypes Ascription of WWW-related interests on the basis of user stereotypes
Empirical Foundations Controlled Evaluations With Users Assessment by users of the relevance of documents supplied by an information filtering system


Ardissono, L., & Goy, A. (1999). Tailoring the interaction with users in electronic shops. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference (pp. 35-44). Vienna, New York: Springer Wien New York. http://www.di.unito.it/~liliana/
Functions Recommend Products or Other Objects General approaches to the recommendation problem


Ardissono, L., Goy, A., Meo, R., Petrone, G., Console, L., Lesmo, L., Simone, C., & Torasso, P. (1999). A configurable system for the construction of adaptive virtual stores. World Wide Web Journal, 2, 143-159. http://www.di.unito.it/~liliana/
Functions Recommend Products or Other Objects General approaches to the recommendation problem


Bares, W. H., & Lester, J. C. (1997). Cinematographic user models for automated realtime camera control in dynamic 3D environments. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 215-226). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Adapt an Interface Translate high-level visualization preferences into concrete camera control actions
Properties General Interests and Preferences User-specific low-level parameters concerning camera control
Input Types Self-Reports on Proficiencies and Interests High-level visualization preferences
Inference Application-Specific Rules Rules for deriving low-level camera directives from visualization preferences
Empirical Foundations Controlled Evaluations With Users Ratings of 3D visualizations produced on the basis of stated visualization preferences


Basu, C., Hirsh, H., Cohen, W. W., & Nevill-Manning, C. (1999). Recommending papers by mining the web. Proceedings of the IJCAI99 Workshop "Learning About Users", Stockholm. http://www.sics.se/humle/ijcai99-ws/
Functions Help U to Find Information Recommend suitable papers to reviewers
Input Types Other Texts by U available from U's Web site
Inference Application-Specific Computations Computation of similarity between a paper and U's own texts
Empirical Foundations Evaluations With an Empirical Database Comparison of several similarity metrics


Bauer, M., & Dengler, D. (1999). InfoBeans: Configuration of personalized information assistants. In M. T. Maybury (Ed.), IUI99: International Conference on Intelligent User Interfaces. New York: ACM.
Functions Help U With Routine Tasks Learn a procedure on the basis of U's demonstration


Bauer, M., Dengler, D., & Paul, G. (2000). Instructible information agents for web mining. In H. Lieberman (Ed.), IUI2000: International Conference on Intelligent User Interfaces. New York: ACM. http://lieber.www.media.mit.edu/people/lieber/IUI/
Functions Help U With Routine Tasks Learn a procedure on the basis of U's demonstration


Beaumont, I. (1994). User modeling in the interactive anatomy tutoring system ANATOM-tutor. User Modeling and User-Adapted Interaction, 4, 21-45.
Functions Tailor Information Presentation Adapt explanations in a tutoring system to U's knowledge level
Support Learning Choose appropriate test questions in a tutoring system
Properties Beliefs U's knowledge of specific anatomy concepts
Input Types Self-Reports on Personal Characteristics Specification of U's position in the course being taken
Responses to Test or Practice Items Answers to questions in a tutoring system
Inference Stereotypes Rules for triggering stereotypes on the basis of U's actions


Beck, J., Stern, M., & Woolf, B. P. (1997). Using the student model to control problem difficulty. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 277-288). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Support Learning Choose math exercises taking into account U's subskill proficiencies
Properties Proficiencies Mastery of particular math subskills
Proficiencies Rates at which U learns and forgets instructional content, respectively
Input Types Responses to Test or Practice Items Answers to math problems; nature of hints required before answering
Inference Application-Specific Computations Calculus for updating assessments of U's subskill proficiencies
Empirical Foundations Controlled Evaluations With Users Formative evaluation of a math tutoring system


Beck, J., Stern, M., & Wolf, B. P. (1997). Cooperative student models. In B. du Boulay & R. Mizoguchi (Eds.), Artificial intelligence in education: Knowledge and media in learning systems (pp. 127-134). Amsterdam: IOI Press.
Functions Support Learning Other systems that employ student modeling
Empirical Foundations Controlled Evaluations With Users Various other controlled evaluations


Benaki, E., Karkaletsis, V. A., & Spyropoulos, C. D. (1997). Integrating user modeling into information extraction: The UMIE prototype. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 55-57). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Help U to Find Information Select documents of interest to U
Input Types Evaluations of Specific Objects Expressed interest in sample documents


Bianchi-Berthouze, N., Berthouze, L., & Kato, T. (1999). Understanding subjectivity: An interactionist view. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference (pp. 3-12). Vienna, New York: Springer Wien New York. http://www.cs.usask.ca/UM99/
Properties Other Properties Modeling of relationship between perception, emotion, and action


Billsus, D., & Pazzani, M. (1998). Learning collaborative information filters. In J. Shavlik (Ed.), Machine learning: Proceedings of the Fifteenth International Conference. San Francisco, CA: Morgan Kaufmann. http://www.ics.uci.edu/~pazzani/Publications/Publications.html
Inference Decision-Theoretic Methods Naive-Bayesian classification


Billsus, D., & Pazzani, M. J. (1999). A hybrid user model for news story classification. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference (pp. 99-108). Vienna, New York: Springer Wien New York. http://www.ics.uci.edu/~pazzani/Publications/Publications.html
Functions Help U to Find Information Recommend news stories
Properties General Interests and Preferences Words typically occurring in stories U finds interesting
Input Types Evaluations of Specific Objects Evaluations of presented news stories
Inference Probability Learning Naive Bayesian learning of probabilities representing U's interests
Instance-Based Learning Nearest neighbor determination of intesting news stories
Empirical Foundations Controlled Evaluations With Users Tests with users of several learning algorithms


Bowne Internet Solutions,  . (1999). Open sesame: Technical white paper.
General Works Overviews Concerning Particular Types of Adaptation Overviews concerning commercially oriented personalized adaptive Web sites


Boyle, C., & Encarnacio, A. O. (1994). MetaDoc: An adaptive hypertext reading system. User Modeling and User-Adapted Interaction, 4, 1-19.
Functions Tailor Information Presentation Adapt quantity of information presented in a documentation system to U's knowledge
Properties Proficiencies U's overall knowledgeability with regard to computer and Unix/AIX concepts
Input Types Self-Reports on Proficiencies and Interests Self-assessment of expertise with regard to computers and Unix/AIX
Evaluations of Specific Objects Optional editing of user model to specify which concepts should be explained in detail
Naturally Occurring Actions U's button presses requesting further information on a concept
Inference Stereotypes Rules for triggering stereotypes on the basis of U's actions


Breese, J., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In G. F. Cooper & S. Moral (Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference (pp. 43-52). San Francisco: Morgan Kaufmann. http://www2.sis.pitt.edu/~dsl/UAI/uai.html
Functions Recommend Products or Other Objects Recommend entertainment objects (e.g., films)
Input Types Evaluations of Specific Objects Evaluations of entertainment objects
Naturally Occurring Actions Web site visits, TV viewing as obtained from Nielsen
Inference Social Recommendation Empirical comparison of collaborative filtering algorithms
Decision-Theoretic Methods For collaborative filtering, 2 types of model
Empirical Foundations Learning Parameters for Users in General BN learning
Learning Parameters for Users in General Collaborative fltering learning


Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6, 87-129.
General Works Overviews Concerning Particular Types of Adaptation Overviews of research on adaptive hypermedia


Brusilovsky, P., & Schwarz, E. (1997). User as student: Towards an adaptive interface for advanced web-based applications. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 177-188). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Adapt an Interface Adapt interface features and hints to U's familiarity with S
Properties Beliefs Knowledge concerning features of a complex interface
Input Types Naturally Occurring Actions U's use of interface features and reading of hints about them


Bull, S. (1997). See Yourself Write: A simple student model to make students think. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 315-326). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Other Functions Provide to students feedback on their strengths and weaknesses in foreign language writing
Properties Beliefs A student's strengths and weaknesses in a subject area (qualitatively and quantitatively assessed)
Input Types Other Explicit assessments of U by a human instructor
Inference Application-Specific Computations Algorithm for generalizing the human instructor's assessments of U's strengths and weaknesses
Empirical Foundations Informal Responses by Early Users Students' comments on an inspectable student model


Bull, S., & Broady, E. (1997). Spontaneous peer tutoring from sharing user models. In B. du Boulay & R. Mizoguchi (Eds.), Artificial intelligence in education: Knowledge and media in learning systems (pp. 143-150). Amsterdam: IOI Press.
Functions Support Learning Other systems that employ student modeling


Bull, S., Pain, H., & Brna, P. (1994). Student modeling beyond domain knowledge. In A. Kobsa & D. Litman (Eds.), Proceedings of the Fourth International Conference on User Modeling (pp. 197-204). Boston, MA: User Modeling, Inc.
Properties Beliefs Effort to expand the scope of what is modeled in student modeling


Bull, S., & Smith, M. (1997). A pair of student models to encourage collaboration. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 339-341). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Support Collaboration Model learners in collaborative learning environments
Properties Proficiencies Ability to handle particular topics individually and in collaboration
Input Types Responses to Test or Practice Items Answers to test questions handled individually or in collaboration
Inference Application-Specific Computations Procedures for summarizing results of tests taken individually and in collaboration


Burke, R. D. (2000). Knowledge-based recommender systems. Encyclopedia of Library and Information Science. To appear. http://www.ics.uci.edu/~burke/research/cbr-ec.html
Functions Recommend Products or Other Objects General approaches to the recommendation problem
Input Types Evaluations of Specific Objects Critiques of proposed solutions


Burke, R. D. (1999). The Wasabi personal shopper: A case-based recommender system. Proceedings of the 11th National Conference on Innovative Applications of Artificial Intelligence, pp. 844-849. http://www.ics.uci.edu/~burke/research/cbr-ec.html
Functions Recommend Products or Other Objects General approaches to the recommendation problem


Burke, R. D., Hammond, K. J., & Young, B. C. (1997). The FindMe approach to assisted browsing. IEEE Expert, 12(4), 32-40.
Functions Recommend Products or Other Objects General approaches to the recommendation problem
Input Types Evaluations of Specific Objects Critiques of proposed solutions


Bushey, R., Mauney, J. M., & Deelman, T. (1999). The development of behaviour-based user models for a computer system. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference (pp. 109-118). Vienna, New York: Springer Wien New York. http://www.cs.usask.ca/UM99/
Empirical Foundations Learning Parameters for Users in General Off-line cluster analyses to identify different user categories


Carberry, S., & Clarke, J. R. (1997). Generating clinical exercises of varying difficulty. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 273-275). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Support Learning Generate medical cases of an appropriate difficulty level
Properties Proficiencies A medical student's level of diagnostic expertise
Input Types Responses to Test or Practice Items Handling of previously presented medical cases
Empirical Foundations Knowledge Acquisition Judgments of an expert surgeon concerning factors that influence the difficulty of medical cases


Cawsey, A. J., Jones, R. B., & Pearson, J. (2000). The evaluation of a personalised health information system for patients with cancer. User Modeling and User-Adapted Interaction, 10, 47-72.
Functions Tailor Information Presentation Adapt health-education documents to individual patients
Properties Personal Characteristics Personal characteristics recorded in U's medical record
Inference Other Inference Methods Text planning techniques
Empirical Foundations Experience With Real-World Use Rating of adaptive and nonadaptive versions of a system by real users


Chajewska, U., Getoor, L., Norman, J., & Shahar, Y. (1998). Utility elicitation as a classification problem. In G. F. Cooper & S. Moral (Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference (pp. 79-88). San Francisco: Morgan Kaufmann. http://www2.sis.pitt.edu/~dsl/UAI/uai.html
Functions Other Functions Support decision making
Properties General Interests and Preferences Utility functions for decision making
Input Types Evaluations of Specific Objects For utility elicitation
Responses to Test or Practice Items Answers to binary-choice questions about preferences
Inference Social Recommendation Assignment to cluster of similar decision makers
Empirical Foundations Learning Parameters for Users in General Collaborative fltering learning
Evaluations With an Empirical Database Test of clustering algorithm for utility functions


Cheverst, K., Davies, N., Mitchell, K., Friday, A., & Efstratiou, C. (2000). Developing a context-aware electronic tourist guide: Some issues and experience. In T. Turner, G. Szwillus, M. Czerwinski, & F. Paterṇ (Eds.), Human factors in computing systems: CHI 2000 conference proceedings (pp. 17-24). New York: ACM.
Properties Context of Interaction Current situation of the user of a tourist guide system


Chin, D. N. (1993). Acquiring user models. Artificial Intelligence Review, 7, 185-197.
General Works Other Overviews Overview of techniques for user model construction


Chiu, B. C., Webb, G. I., & Kuzmycz, M. (1997). A comparison of first-order and zeroth-order induction for input-output agent modelling. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 347-358). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Other Functions Predict correct and incorrect answers of a student
Properties Beliefs A student's knowledge of particular problem-solving rules
Input Types Responses to Test or Practice Items Answers to subtraction problems
Empirical Foundations Evaluations With an Empirical Database Comparative evaluation of two systems' success in analyzing students' performance on subtraction problems


Cohen, R., Schmidt, K., & Beek, P. v. (1994). A framework for soliciting clarification from users during plan recognition. In A. Kobsa & D. Litman (Eds.), Proceedings of the Fourth International Conference on User Modeling (pp. 11-17). Boston, MA: User Modeling, Inc.
Functions Control Dialog Decide when and how to elicit further information from U


Collins, J. A., Greer, J. E., Kumar, V. S., McCalla, G. I., Meagher, P., & Tkatch, R. (1997). Inspectable user models for just-in-time workplace training. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 327-337). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Support Collaboration Select appropriate collaborators (or help U to do so) and facilitate communication between collaborators
Properties Personal Characteristics Location, job title, etc., of potential collaborators
Beliefs Ability to perform specific steps of a task
Input Types Self-Reports on Personal Characteristics Job title, level of education, etc.
Other Self-Reports Self-reports on the successful completion of specific subtasks


Conati, C., Gertner, A. S., VanLehn, K., & Druzdzel, M. J. (1997). On-line student modeling for coached problem solving using Bayesian networks. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 231-242). Vienna: Springer Wien New York. http://www.cs.ubc.ca/~conati/publications.html
Functions Support Learning Tailor instructional interventions to U's state of knowledge and current goals
Properties Current Goal Problem solving strategy currently pursued by U
Beliefs A student's knowledge of particular problem-solving rules
Input Types Responses to Test or Practice Items Observable steps in a student's problem solving
Inference Decision-Theoretic Methods Bayesian networks for inferences about unobservable aspects of a student's problem solving
Empirical Foundations Evaluations With an Empirical Database Study of the feasability of the use of approximative inference algorithms


Conati, C., Larkin, J. H., & VanLehn, K. (1997). A computer framework to support self-explanation. In B. du Boulay & R. Mizoguchi (Eds.), Artificial intelligence in education: Knowledge and media in learning systems (pp. 279-286). Amsterdam: IOI Press. http://www.cs.ubc.ca/~conati/publications.html
Functions Support Learning Tailor instructional interventions to U's state of knowledge and current goals
Properties Proficiencies U's general physics knowledge
Beliefs U's beliefs about physics relevant to current problem
Inference Decision-Theoretic Methods Bayesian networks for inferences about unobservable aspects of a student's problem solving


Cook, R., & Kay, J. (1994). The justified user model: A viewable, explained user model. In A. Kobsa & D. Litman (Eds.), Proceedings of the Fourth International Conference on User Modeling (pp. 145-150). Boston, MA: User Modeling, Inc.
Functions Give Feedback Display model of U's knowledge of an application


Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modelling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4, 253-278.
Functions Support Learning Tutor U in a programming language, adapting feedback, hints, and exercises


Corbett, A. T., & Bhatnagar, A. (1997). Student modeling in the ACT programming tutor: Adjusting a procedural learning model with declarative knowledge. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 243-254). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Support Learning Support mastery learning by tracing the development of U's knowledge
Other Functions Show LISP learners estimates of their mastery of various skills
Properties Proficiencies Command of the declarative knowledge relevant to problem solving
Beliefs A student's knowledge of particular problem-solving rules
Input Types Responses to Test or Practice Items Performance of a student when given an opportunity to apply a given production rule
Inference Application-Specific Computations Computation of declarative knowledge factor scores
Empirical Foundations Results of Previous Research Assessment of accuracy of knowledge tracing predictions that do not take declarative knowledge into account
Evaluations With an Empirical Database Assessment of accuracy of knowledge tracing predictions that take declarative knowledge into account


De Bra, P., Brusilovsky, P., & Houben, G. (1999). Adaptive hypermedia: From systems to framework. ACM Computing Surveys, 31(4es).
General Works Overviews Concerning Particular Types of Adaptation Overviews of research on adaptive hypermedia


De Carolis, B., & Pizzutilo, S. (1997). From discourse plans to user-adapted hypermedia. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 37-39). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Help U to Find Information Adapt hypertext navigation mode to U's knowledge
Tailor Information Presentation Adapt hypermedia presentations to U's interests and knowledge
Properties Personal Characteristics Level of education, age, etc.
Beliefs U's overall familiarity with the subject matter of a hypertext
Input Types Self-Reports on Proficiencies and Interests Self-assessments of domain and system competence


Dey, A. K., Abowd, G. D., & Wood, A. (1998). Cyberdesk: A framework for providing self-integrating context-aware services. Knowledge-Based Systems, 11, 3-13. http://www.cc.gatech.edu/fce/cyberdesk/
Functions Adapt an Interface Suggest uses of Web-based applications that are appropriate to U's current working context
Properties Context of Interaction Work U is currently performing; U's location
Input Types Low-Level Measures of Context Information from running applications; position sensors


Dieterich, H., Malinowski, U., Kühme, T., & Schneider-Hufschmidt, M. (1993). State of the art in adaptive user interfaces. In M. Schneider-Hufschmidt, T. Kühme, & U. Malinowski (Eds.), Adaptive user interfaces (pp. 13-48). The Netherlands: Elsevier Science Publishers.
General Works Overviews Concerning Particular Types of Adaptation Overview that covers adaptive information presentation as well as interface adaptation; includes a detailed taxonomy


Dimitrova, V., Self, J., & Brna, P. (1999). The interactive maintenance of open learner models. In S. P. Lajoie & M. Vivet (Eds.), Artificial intelligence in education: Open learning environments: new computational technologies to support learning, exploration, and collaboration (pp. 405-412). Amsterdam: IOI Press.
Functions Give Feedback Interactive student modeling


Dix, A. J., Finlay, J. E., Abowd, G. D., & Beale, R. (1998). Human-computer interaction (2nd edition). New York: Prentice-Hall.
General Works Expositions of methodology for user studies in HCI Comprehensive volumes, each with several relevant chapters


Doux, A., Laurent, J., & Nadal, J. (1997). Symbolic data analysis with the K-means algorithm for user profiling. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 359-361). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Adapt an Interface Recommend settings for technical devices
Properties Other Properties Assignment to one of a set of classes of similar users of technical devices
Input Types Naturally Occurring Actions Choices of device settings in various environments
Naturally Occurring Actions Behavior with technical devices in various environments
Inference Other Machine Learning Techniques Use of a variant of the K-Means algorithm to classify users
Empirical Foundations Evaluations With an Empirical Database Evaluation of an ML technique's performance on real and simulated data


Duncan, D., Brna, P., & Morss, L. (1994). A Bayesian approach to diagnosing problems with Prolog control flow. In A. Kobsa & D. Litman (Eds.), Proceedings of the Fourth International Conference on User Modeling (pp. 79-86). Boston, MA: User Modeling, Inc.
Functions Support Learning Other systems that employ student modeling
Empirical Foundations Controlled Evaluations With Users Various other controlled evaluations


Elzer, S., Chu-Carroll, J., & Carberry, S. (1994). Recognizing and utilizing user preferences in collaborative consultation dialogues. In A. Kobsa & D. Litman (Eds.), Proceedings of the Fourth International Conference on User Modeling (pp. 19-24). Boston, MA: User Modeling, Inc.
Functions Recommend Products or Other Objects General approaches to the recommendation problem


Fiedler, A. (1999). Using a cognitive architecture to plan dialogs for the adaptive explanation of proofs. In T. Dean (Ed.), Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (pp. 358-363). San Francisco, CA: Morgan Kaufmann.
Functions Tailor Information Presentation Verbalize mathematical proofs in accordance with U's knowledge
Properties Beliefs Specific aspects of declarative and procedural knowledge of U about mathematical proofs
Input Types Naturally Occurring Actions Questions about a presented mathematical proof
Inference Other Inference Methods Use of ACT-R to simulate U's understanding of a mathematical proof


Fink, J., & Kobsa, A. (2000). A review and analysis of commercial user modeling servers for personalization on the world wide web. User Modeling and User-Adapted Interaction, 10, 209-249.
General Works Overviews Concerning Particular Types of Adaptation Overviews concerning commercially oriented personalized adaptive Web sites


Fink, J., Kobsa, A., & Nill, A. (1997). Adaptable and adaptive information access for all users, including the disabled and the elderly. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 171-173). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Tailor Information Presentation Adapt hypermedia presentations to U's interests and knowledge
Adapt an Interface Adapt a hypermedia interface to U's disabilities
Properties General Interests and Preferences Preferences concerning the modality of hypermedia-presented information
General Interests and Preferences General goals motivating U's use of a hypermedia system
Proficiencies Competence in dealing with computers and with a specific hypermedia system
Noncognitive Abilities Perceptual and motor abilities relevant to both computer use and real-world activities
Beliefs U's knowledge of domain concepts relevant to a hypermedia presentation
Input Types Self-Reports on Personal Characteristics Information about personal characteristics related to hypermedia use
Self-Reports on Proficiencies and Interests Preferences and goals concerning hypermedia presentations
Self-Reports on Proficiencies and Interests Self-reports on disabilities
Naturally Occurring Actions Dialog actions in use of a hypermedia system
Inference Stereotypes Ascription of properties associated with types of hypermedia users
Application-Specific Rules Principles for inferring knowledge on the basis of dialog acts


Fleming, M., & Cohen, R. (1999). User modeling in the design of interactive interface agents. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference (pp. 67-76). Vienna, New York: Springer Wien New York. http://www.cs.usask.ca/UM99/
Functions Help U With Routine Tasks Help U to handle e-mail and perform other routine tasks
Properties Behavioral Regularities Regularities in performing routine tasks such as e-mail managment
Input Types Naturally Occurring Actions Acceptance or rejection of assistant's proposed actions
Inference Rule Learning Learning of regularities in U's performance of routine tasks
Application-Specific Rules Algorithm for deciding whether to ask U to help with adaptation decision


Friedman, N., Murphy, K., & Russell, S. (1998). Learning the structure of dynamic probabilistic networks. In G. F. Cooper & S. Moral (Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference (pp. 139-147). San Francisco: Morgan Kaufmann. http://www2.sis.pitt.edu/~dsl/UAI/uai.html
Functions Help U With Routine Tasks Drive U's car
Properties Current Goal Goals of other drivers
Input Types Naturally Occurring Actions Actions of drivers of other cars
Low-Level Measures of Context Velocities, etc., of cars
Inference Decision-Theoretic Methods Learned DBNs
Empirical Foundations Learning Parameters for Users in General BN learning


Gertner, A. S., Conati, C., & VanLehn, K. (1998). Procedural help in Andes: Generating hints using a Bayesian network student model. Proceedings of the Fifteenth National Conference on Artificial Intelligence, Madison, WI. http://www.cs.ubc.ca/~conati/publications.html
Functions Support Learning Tailor instructional interventions to U's state of knowledge and current goals
Properties Proficiencies U's general physics knowledge
Current Goal Problem solving strategy currently pursued by U
Beliefs U's beliefs about physics relevant to current problem
Input Types Responses to Test or Practice Items Observable steps in a student's problem solving
Inference Decision-Theoretic Methods Bayesian networks for inferences about unobservable aspects of a student's problem solving
Empirical Foundations Controlled Evaluations With Users Various other controlled evaluations
Experience With Real-World Use Studies of use of a physics tutor while doing homework


Giangrandi, P., & Tasso, C. (1997). Managing temporal knowledge in student modeling. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 415-426). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Support Learning Take into account changes in a student's beliefs
Properties Beliefs Beliefs held during particular (underspecified) time intervals
Input Types Responses to Test or Practice Items Student actions or utterances that imply possession of a particular belief at a given time
Inference Logic Algorithms for making (nonmonotonic) inferences about beliefs held in particular time intervals


Gori, M., Maggini, M., & Martinelli, E. (1997). Web-browser access through voice input and page interest prediction. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 17-19). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Help U to Find Information Implicitly recommend Web hyperlinks
Adapt an Interface Facilitate selection of presumably relevant Web hyperlinks
Input Types Naturally Occurring Actions Hypermedia pages that U has visited
Inference Neural Networks and Related Techniques Use of recurrent neural networks to summarize U's Web navigation behavior


Grasso, F. (1997). Using dialectical argumentation for user modelling in decision support systems. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 83-85). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Tailor Information Presentation Tailor arguments to U's beliefs and values
Properties General Interests and Preferences Attitudes concerning medical issues
Beliefs U's factual beliefs about medical issues


Greening, D. R. (2000). Building consumer trust with accurate product recommendations: A white paper on Macromedia's LikeMinds personalization technology. Boston: Macromedia. http://www.macromedia.com/
Functions Recommend Products or Other Objects Recommend entertainment objects (e.g., films)
Inference Social Recommendation Description of algorithms used in LikeMinds server


Greer, J. E., McCalla, G. I., Collins, J. A., Kumar, V. S., Meagher, P., & Vassileva, J. (1998). Supporting peer help and collaboration in distributed workplace environments. International Journal of AI and Education, 9, 159-177. http://julita.usask.ca/homepage/Agents.htm
Functions Support Collaboration Select appropriate collaborators (or help U to do so) and facilitate communication between collaborators
Properties Personal Characteristics Location, job title, etc., of potential collaborators
Beliefs Ability to perform specific steps of a task
Input Types Self-Reports on Personal Characteristics Job title, level of education, etc.
Other Self-Reports Self-reports on the successful completion of specific subtasks
Empirical Foundations Controlled Evaluations With Users Various other controlled evaluations


Grüninger, C., & Treeck, W. v. (1993). Contributions of a social science based evaluation for adaptive design projects. In M. Schneider-Hufschmidt, T. Kühme, & U. Malinowski (Eds.), Adaptive user interfaces (pp. 319-330). The Netherlands: Elsevier Science Publishers.
General Works Other Overviews One of the few systematic discussions of evaluation issues concerning user-adaptive systems


Gutkauf, B., Thies, S., & Domik, G. (1997). A user-adaptive chart editing system based on user modeling and critiquing. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 159-170). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Tailor Information Presentation Adapt appearance of charts to U's abilities and preferences
Properties General Interests and Preferences Preferences concerning aspects of charts
Noncognitive Abilities Visual perceptual abilities; mental rotation ability
Input Types Evaluations of Specific Objects Critiques of proposed solutions
Responses to Test or Practice Items Responses to game-like ability tests
Inference Application-Specific Computations Procedures for the interpretation of perceptual ability tests
Empirical Foundations Controlled Evaluations With Users Evaluation of use of an adaptive chart-editing system


Ha, V., & Haddawy, P. (1998). Towards case-based preference elicitation: Similarity measures on preference structures. In G. F. Cooper & S. Moral (Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference (pp. 193-201). San Francisco: Morgan Kaufmann. http://www2.sis.pitt.edu/~dsl/UAI/uai.html
Functions Recommend Products or Other Objects Recommend entertainment objects (e.g., films)
Other Functions Support decision making
Properties General Interests and Preferences Evaluation criteria for movies, etc.
Input Types Evaluations of Specific Objects Evaluations of entertainment objects
Inference Social Recommendation Assignment of U to a cluster of Us with similar preferences, on basis of some expressed preferences
Empirical Foundations Evaluations With an Empirical Database Test of clustering method for preference structures


Heckerman, D., & Horvitz, E. (1998). Inferring informational goals from free-text queries: A Bayesian approach. In G. F. Cooper & S. Moral (Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference (pp. 230-237). San Francisco: Morgan Kaufmann. http://www2.sis.pitt.edu/~dsl/UAI/uai.html
Functions Give Help Suggest help topics on the basis of U's queries
Properties Current Goal U's current goal in using an application
Input Types Naturally Occurring Actions Queries to a help system
Other Queries to a help system
Inference Decision-Theoretic Methods Naive-Bayesian classification
Empirical Foundations Knowledge Acquisition Acquisition of probabilities for Bayesian inference from domain experts


Helander, M. (Ed.) (1988). Handbook of human-computer interaction. Amsterdam: North-Holland.
General Works Expositions of methodology for user studies in HCI Comprehensive volumes, each with several relevant chapters


Helander, M., Landauer, T. K., & Prabhu, P. V. (Eds.) (1997). Handbook of human-computer interaction (second, completely revised edition). Amsterdam: North-Holland.
General Works Expositions of methodology for user studies in HCI Comprehensive volumes, each with several relevant chapters


Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. Proceedings of the 1999 Conference on Research and Development in Information Retrieval. http://www.cs.umn.edu/Research/GroupLens/
Inference Social Recommendation Comparison of several algorithms


Hirsh, H., & Davison, B. D. (1997). An adaptive UNIX command-line assistant. Proceedings of AGENTS'97.
Functions Help U With Routine Tasks Predict U's commands so as to facilitate execution


Hirst, G., DiMarco, C., Hovy, E., & Parsons, K. (1997). Authoring and generating health-education documents that are tailored to the needs of the individual patient. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 107-118). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Tailor Information Presentation Adapt health-education documents to individual patients
Properties Personal Characteristics Personal characteristics recorded in U's medical record
General Interests and Preferences Attitudes concerning medical issues
Input Types Other U's medical record


Hohl, H., Böcker, H., & Gunzenhauser, R. (1996). Hypadapter: An adaptive hypertext system for exploratory learning and programming. User Modeling and User-Adapted Interaction, 6, 131-156.
Functions Support Learning Support exploratory learning (e.g., of LISP) by providing appropriate information and examples
Properties General Interests and Preferences U's preferences and interests related to the learning of LISP
Proficiencies U's proficiency with respect to various aspects of programming
Beliefs U's knowledge of specific LISP concepts
Input Types Self-Reports on Proficiencies and Interests Self-assessment of expertise in LISP
Naturally Occurring Actions Selection of links and definitions of bookmarks in an instructional system
Inference Stereotypes Rules for triggering stereotypes on the basis of U's actions


Hook, K., Karlgren, J., Waern, A., Dahlbäck, N., Jansson, C. G., Karlgren, K., & Lemaire, B. (1996). A glass box approach to adaptive hypermedia. User Modeling and User-Adapted Interaction, 6, 157-184.
Functions Support Learning Provide adaptive hypermedia help to U during the use of a complex application
Properties Current Goal U's current task in using a complex application (formulated in domain-specific terms)
Input Types Other Self-Reports Explicit, domain-specific specification of current information need
Other Queries to a help system
Inference Other Inference Methods Plan recognition methods for inferring U's goal from her queries


Horvitz, E. (1999). Principles of mixed-initiative user interfaces. In M. G. Williams, M. W. Altom, K. Ehrlich, & W. Newman (Eds.), Human factors in computing systems: CHI '99 conference proceedings (pp. 159-166). New York: ACM. http://research.microsoft.com/~horvitz/UIACT.HTM
Functions Help U With Routine Tasks Help schedule meetings by suggesting meeting parameters (e.g., duration)
Inference Decision-Theoretic Methods Decision-theoretic methods for deciding when to initiate interaction


Horvitz, E. (1999). Uncertainty, action, and interaction: In pursuit of mixed-initiative computing. Intelligent Systems. http://research.microsoft.com/~horvitz/UIACT.HTM
Functions Help U With Routine Tasks Help schedule meetings by suggesting meeting parameters (e.g., duration)
Inference Decision-Theoretic Methods Decision-theoretic methods for deciding when to initiate interaction


Horvitz, E., Breese, J., Heckerman, D., Hovel, D., & Rommelse, K. (1998). The Lumière project: Bayesian user modeling for inferring the goals and needs of software users. In G. F. Cooper & S. Moral (Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference (pp. 256-265). San Francisco: Morgan Kaufmann. http://www2.sis.pitt.edu/~dsl/UAI/uai.html
Functions Give Help Volunteer help in using various applications
Other Functions U's momentary willingness to be interrupted
Properties Proficiencies U's domain competence
Current Goal U's current goal in using an application
Input Types Naturally Occurring Actions Actions in using Office applications
Other Natural language queries about the application being used
Inference Decision-Theoretic Methods Bayesian networks for inferring an Office user's current goals
Empirical Foundations Early Exploratory Studies Wizard-of-Oz study of expert assistance


Horvitz, E., Jacobs, A., & Hovel, D. (1999). Attention-sensitive alerting. In K. B. Laskey & H. Prade (Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fifteenth Conference (pp. 305-313). San Francisco: Morgan Kaufmann. http://www2.sis.pitt.edu/~dsl/UAI/uai.html
Functions Other Functions Adapt computer alerts to U's current state


Jameson, A. (1996). Numerical uncertainty management in user and student modeling: An overview of systems and issues. User Modeling and User-Adapted Interaction, 5, 193-251. http://www.cs.uni-sb.de/users/jameson/
General Works Overviews Concerning Particular Types of Inference Overview of use of Bayesian networks, Dempster-Shafer theory and fuzzy logic for user-adaptive systems


Joachims, T., Freitag, D., & Mitchell, T. (1996). WebWatcher: A tour guide for the World Wide Web. Technical Report CMU-CS-96-xxx. Pittsburgh, PA: School of Computer Science, Carnegie Mellon University. http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-6/web-agent/www/project-home.html
Functions Help U to Find Information Recommend Web pages
Empirical Foundations Experience With Real-World Use Various other evaluations of real-world use


Joachims, T., Freitag, D., & Mitchell, T. (1997). WebWatcher: A tour guide for the World Wide Web. In M. E. Pollack (Ed.), Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (pp. 770-777). San Francisco, CA: Morgan Kaufmann. http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-6/web-agent/www/project-home.html
Functions Help U to Find Information Recommend Web pages
Empirical Foundations Experience With Real-World Use Various other evaluations of real-world use


Johnson, C. (1999). The application of user modeling techniques to reason about the human contribution to major accidents. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference (pp. 13-22). Vienna, New York: Springer Wien New York. http://www.cs.usask.ca/UM99/
Inference Logic Representation with epistemic logic of beliefs of operators involved in a near-accident


Jones, R., Pearson, J., McGregor, S., Cawsey, A. J., Barrett, A., Craig, N., Atkinson, J. M., Gilmour, W. H., & McEwen, J. (1999). Randomised trial of personalised computer based information for cancer patients. British Medical Journal, 319, 1241-1247. http://www.bmj.com/cgi/content/full/319/7219/1241
Functions Tailor Information Presentation Adapt health-education documents to individual patients
Properties Personal Characteristics Personal characteristics recorded in U's medical record
Inference Other Inference Methods Text planning techniques
Empirical Foundations Experience With Real-World Use Rating of adaptive and nonadaptive versions of a system by real users


Jordan, P. W., Thomas, B., Weerdmeester, B. A., & McClelland, I. L. (Eds.) (1996). Usability evaluation in industry. London: Taylor & Francis.
General Works Expositions of methodology for user studies in HCI Comprehensive volumes, each with several relevant chapters


Kalyuga, S., Chandler, P., & Sweller, J. (1997). Levels of expertise and user-adapted formats of instructional presentations: A cognitive load approach. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 261-272). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Tailor Information Presentation Choose appropriate form of presentation with text and/or diagrams
Properties Proficiencies Level of domain expertise
Empirical Foundations Results of Previous Research Experiments on relationships between expertise, presentation format, and comprehension by users


Kaplan, C., Fenwick, J., & Chen, J. (1993). Adaptive hypertext navigation based on user goals and context. User Modeling and User-Adapted Interaction, 3, 193-220.
Functions Help U to Find Information Help U prioritize the links in a hypertext system
Properties Current Goal U's current goal and/or topic of interest in using a hypertext system
Input Types Evaluations of Specific Objects Expressions of interest in particular hypertext topics
Other Self-Reports Explicit specification of current goal
Naturally Occurring Actions Time spent visiting hypertext pages
Inference Application-Specific Computations Procedure for assessing U's interests on basis of time spent viewing pages


Kay, J. (1994). Lies, damned lies, and stereotypes: Pragmatic approximations of users. In A. Kobsa & D. Litman (Eds.), Proceedings of the Fourth International Conference on User Modeling (pp. 175-184). Boston, MA: User Modeling, Inc.
General Works Overviews Concerning Particular Types of Inference Relatively recent discussion of the stereotype approach


Kobsa, A., Müller, D., & Nill, A. (1994). KN-AHS: An adaptive hypertext client of the user modeling system BGP-MS. In A. Kobsa & D. Litman (Eds.), Proceedings of the Fourth International Conference on User Modeling (pp. 99-104). Boston, MA: User Modeling, Inc.
Functions Tailor Information Presentation Adapt hypertext to U's domain knowledge
Properties Beliefs Knowledge of particular concepts
Inference Stereotypes Use of shell system BGP-MS


Kobsa, A., & Pohl, W. (1995). The user modeling shell system BGP-MS. User Modeling and User Adapted Interaction, 4, 59-106.
General Works Overviews Concerning Particular Types of Inference Detailed discussion of a well-known user modeling shell system that deals with many of the general issues raised by the use of logic and stereotypes


Kobsa, A., & Wahlster, W. (Eds.) (1989). User models in dialog systems. Berlin: Springer.
General Works Edited Volumes The first edited volume on user modeling, focusing mainly on natural language dialog systems


Küpper, D., & Kobsa, A. (1999). User-tailored plan generation. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference (pp. 45-54). Vienna, New York: Springer Wien New York. http://www.cs.usask.ca/UM99/
Functions Give Help Generate user-adapted action plans
Inference Logic Reasoning about stereotypes with modal logic
b=Hustadt94


Langley, P. (1999). User modeling in adaptive interfaces. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference. Vienna, New York: Springer Wien New York. http://www.cs.usask.ca/UM99/
General Works Overviews Concerning Particular Types of Inference Overview of a number of applications of machine learning techniques to user-adaptive systems


Lau, T., & Horvitz, E. (1999). Patterns of search: Analyzing and modeling Web query dynamics. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference (pp. 119-128). Vienna, New York: Springer Wien New York. http://www.cs.usask.ca/UM99/
Functions Help U to Find Information Recommend Web pages
Properties Current Goal Goals of U's Web searches
Input Types Naturally Occurring Actions U's goal in searching the Web
Inference Decision-Theoretic Methods Empirically derived Bayesian networks
Empirical Foundations Learning Parameters for Users in General Learning of Bayes nets from WWW log data


Lesh, N., Rich, C., & Sidner, C. L. (1999). Using plan recognition in human-computer collaboration. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference (pp. 23-32). Vienna, New York: Springer Wien New York. http://www.cs.usask.ca/UM99/
Functions Give Help Remind U of previously recognized plans
Properties Current Goal U's plans in using an e-mail system
Input Types Naturally Occurring Actions U's actions in using an e-mail system
Inference Other Inference Methods Plan recognition
Empirical Foundations Early Exploratory Studies Informal evaluation of e-mail plan recognizer


Linden, G., Hanks, S., & Lesh, N. (1997). Interactive assessment of user preference models: The automated travel assistant. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 67-78). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Recommend Products or Other Objects Recommend potentially suitable existing solutions to a problem (e.g., airplane flights)
Properties General Interests and Preferences Preferences concerning possible solutions to a problem
Input Types Evaluations of Specific Objects Critiques of proposed solutions
Inference Other Inference Methods Principled method for elicitation and interpretation of critiques of proposed solutions
Empirical Foundations Experience With Real-World Use Responses to a flight recommendation system by Web users


Litman, D. J., & Pan, S. (2000). Predicting and adapting to poor speech recognition in a spoken dialogue system. Proceedings of the Seventeenth National Conference on Artificial Intelligence, Austin, TX, pp. 722-728. http://www.research.att.com/~diane/dslearning-pubs.html
Functions Control Dialog Adapt dialog strategy to the current likelihood of successful dialog completion
Properties Other Properties U's likelihood of completing the current dialog successfully
Input Types Naturally Occurring Actions Various features of U's behavior in a spoken dialog
Inference Rule Learning  
Empirical Foundations Controlled Evaluations With Users Various other controlled evaluations


Lucente, M. (2000). Conversational interfaces for e-commerce applications. Communications of the ACM, 43(9), 59-61.
Functions Recommend Products or Other Objects Conduct a product recommendation dialog in natural language


Maes, P. (1994). Agents that reduce work and information overload. Communications of the ACM, 37(7), 30-40.
Functions Help U With Routine Tasks Help U to handle e-mail and perform other routine tasks
Properties Behavioral Regularities Regularities in performing routine tasks such as e-mail managment
Input Types Naturally Occurring Actions Acceptance or rejection of assistant's proposed actions
Inference Rule Learning Learning of regularities in U's performance of routine tasks
Empirical Foundations Experience With Real-World Use Evaluation of ML predictions in actual use


Maes, P., Guttman, R. H., & Moukas, A. G. (1999). Agents that buy and sell. Communications of the ACM, 42(3), 81-91.
General Works Overviews Concerning Particular Types of Adaptation Overviews concerning commercially oriented personalized adaptive Web sites


Maglio, P. P., & Barrett, R. (1997). How to build modeling agents to support web searchers. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 5-16). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Help U to Find Information Remind U of previous Web navigation paths
Adapt an Interface Offer Web navigation shortcuts that reflect past accesses
Input Types Naturally Occurring Actions Hypermedia pages that U has visited
Empirical Foundations Results of Previous Research Observation of Web navigation behavior


Maglio, P. P., Barrett, R., Campbell, C. S., & Selker, T. (2000). SUITOR: An attentive information system. In H. Lieberman (Ed.), IUI2000: International Conference on Intelligent User Interfaces (pp. 169-176). New York: ACM. http://lieber.www.media.mit.edu/people/lieber/IUI/
Functions Help U to Find Information Recommend Web pages
Input Types Low-Level Measures of Psychological States Gaze as an indicator of what U is currently reading


Manber, U., Patel, A., & Robison, J. (2000). Experience with personalization on Yahoo! Communications of the ACM, 43(8), 35-39.
Functions Tailor Information Presentation Relate information about sports, movies, etc., to U's home location


Mathé, N., & Chen, J. R. (1996). User-centered indexing for adaptive information access. User Modeling and User-Adapted Interaction, 6, 225-261.
Functions Help U to Find Information Help U access known pages within a hypermedia information system
Properties Other Properties Concepts used by U to organize known documents, and assessed relevance of particular parts of documents to particular combinations of these concepts
Input Types Evaluations of Specific Objects Relevance feedback for document parts returned in response to queries
Other Explicit classification of document parts in terms of concepts
Inference Application-Specific Computations Formulas for deriving and updating relevance weights within an Adaptive Relevance Network
Application-Specific Rules Heuristics for automatically adding intermediate nodes to an Adaptive Relevance Network so as to improve its performance


McCalla, G. I., Greer, J. E., Kumar, V. S., Meagher, P., Collins, J. A., Tkatch, R., & Parkinson, B. (1997). A peer help system for workplace training. In B. du Boulay & R. Mizoguchi (Eds.), Artificial intelligence in education: Knowledge and media in learning systems (pp. 183-190). Amsterdam: IOI Press.
Functions Support Learning Other systems that employ student modeling
Support Collaboration Select appropriate collaborators (or help U to do so) and facilitate communication between collaborators
Properties Personal Characteristics Location, job title, etc., of potential collaborators
Beliefs Ability to perform specific steps of a task
Input Types Self-Reports on Personal Characteristics Job title, level of education, etc.
Other Self-Reports Self-reports on the successful completion of specific subtasks


Milosavljevic, M. (1997). Augmenting the user's knowledge via comparison. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 119-130). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Tailor Information Presentation Adapt comparisons in text to U's knowledge
Properties Beliefs U's knowledge of domain concepts relevant to a hypermedia presentation
Input Types Naturally Occurring Actions Hypermedia pages that U has visited


Mitchell, T., Caruana, R., Freitag, D., McDermott, J., & Zabowski, D. (1994). Experience with a learning personal assistant. Communications of the ACM, 37(7), 81-91.
Functions Help U With Routine Tasks Help schedule meetings by suggesting meeting parameters (e.g., duration)
Properties Behavioral Regularities Regularities in U's meeting-scheduling behavior
Input Types Naturally Occurring Actions U's choices in scheduling meetings
Inference Rule Learning Learning of regularities in U's performance of routine tasks
Empirical Foundations Experience With Real-World Use Evaluation of ML predictions in actual use


Mitchell, J., & Shneiderman, B. (1989). Dynamic versus static menus: An exploratory comparison. SIGCHI Bulletin, 20(4), 33-37.
Functions Adapt an Interface Studies of adaptive menus
Empirical Foundations Controlled Evaluations With Users Various other controlled evaluations


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Functions Support Learning Other systems that employ student modeling
Support Collaboration Model learners in collaborative learning environments


Moinard, C., & Joab, M. (1997). Dynamic assessment for operator training. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 255-257). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Support Learning Derive a differentiated assessment of a trainee's problem solving skills
Properties Beliefs Characterization of skills at the operational, tactical, and strategic levels.
Input Types Responses to Test or Practice Items Problem-solving actions within a training system
Inference Application-Specific Computations Comparison of a trainee's actions with those of an expert problem solving module


Motoda, H., & Yoshida, K. (1998). Machine learning techniques to make computers easier to use. In (Ed.),
Functions Help U With Routine Tasks Predict U's commands so as to facilitate execution
Properties Behavioral Regularities Typical command sequences
Input Types Naturally Occurring Actions Commands
Inference Other Machine Learning Techniques Graph-based induction


Mühlenbrock, M., Tewissen, F., & Hoppe, H. U. (1997). A framework system for intelligent support in open distributed learning environments. In B. du Boulay & R. Mizoguchi (Eds.), Artificial intelligence in education: Knowledge and media in learning systems (pp. 191-198). Amsterdam: IOI Press.
Functions Support Learning Other systems that employ student modeling


Murphy, M., & McTear, M. (1997). Learner modelling for intelligent CALL. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 301-312). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Tailor Information Presentation Adapt handling of incorrect answers to the student's preferences
Support Learning Select suitable language exercises
Properties Personal Characteristics Level of education, age, etc.
General Interests and Preferences Preferences and attitudes concerning aspects of language learning
Proficiencies Proficiency with respect to the target language and the domain
Beliefs Proneness to and causes of particular language errors
Input Types Self-Reports on Personal Characteristics Job title, level of education, etc.
Self-Reports on Proficiencies and Interests Preferences and attitudes concerning aspects of language learning
Self-Reports on Proficiencies and Interests Self-assessments of language proficiencies and motivation
Responses to Test or Practice Items Performance on language test items
Inference Stereotypes Derivation of initial proficiency estimates on the basis of U's overall level of advancement
Application-Specific Computations Computational procedures for estimating proficiencies and error-pronenesses
Empirical Foundations Informal Responses by Early Users Learners' responses to a commercial adaptive CALL system


Noh, S., & Gmytrasiewicz, P. J. (1997). Agent modeling in antiair defense: A case study. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 389-400). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Other Functions Anticipate other agents' actions so as to coordinate with them
Properties General Interests and Preferences Payoff matrices that underlie U's behavior
Other Properties U's higher-order beliefs about the system's payoffs and beliefs
Empirical Foundations Evaluations With an Empirical Database Comparison of the Recursive Modeling Method with simpler methods
Controlled Evaluations With Users Comparison of the Recursive Modeling Method with human performance


Noh, S., & Gmytrasiewicz, P. J. (1999). Towards flexible multi-agent decision-making under time pressure. In T. Dean (Ed.), Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (pp. 492-498). San Francisco, CA: Morgan Kaufmann.
Inference Other Machine Learning Techniques Use of Input-Output Agent Modeling to derive a theory of a student's subtraction knowledge


Ogata, H., & Yano, Y. (1997). Knowledge awareness filtering toward efficient collaborative learning. In B. du Boulay & R. Mizoguchi (Eds.), Artificial intelligence in education: Knowledge and media in learning systems (pp. 207-214). Amsterdam: IOI Press.
Functions Support Learning Other systems that employ student modeling


Paiva, A. (1997). Learner modelling for collaborative learning environments. In B. du Boulay & R. Mizoguchi (Eds.), Artificial intelligence in education: Knowledge and media in learning systems (pp. 215-222). Amsterdam: IOI Press.
Functions Support Learning Other systems that employ student modeling
Support Collaboration Model learners in collaborative learning environments


Paranagama, P., Burstein, F., & Arnott, D. (1997). Modelling the personality of decision makers for active decision support. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 79-81). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Tailor Information Presentation Present decision-supporting information according to U's personality and preferences
Properties Personal Characteristics Personality type
General Interests and Preferences Weights of decision-relevant attributes
Input Types Evaluations of Specific Objects Critiques of proposed solutions
Inference Neural Networks and Related Techniques Use of neural networks to adapt the system's profile of a decision maker
Empirical Foundations Results of Previous Research Study of relationships between personality variables and decision making behavior


Pazzani, M., & Billsus, D. (1997). Learning and revising user profiles: The identification of interesting web sites. Machine Learning, 27, 313-331. http://www.ics.uci.edu/~pazzani/Publications/Publications.html
Functions Help U to Find Information Recommend Web pages
Properties General Interests and Preferences Words typically occurring in stories U finds interesting
Input Types Evaluations of Specific Objects Evaluations of visited web pages
Inference Probability Learning Naive Bayesian learning of probabilities representing U's interests
Decision-Theoretic Methods Naive-Bayesian classification
Empirical Foundations Evaluations With an Empirical Database Comparison of several machine learning techniques


Pazzani, M. J., & Billsus, D. (1999). Adaptive web site agents. Proceedings of the Third International Conference on Autonomous Agents.
Functions Help U to Find Information Recommend publications for downloading from a web site
Properties General Interests and Preferences Preferences concerning the criteria for S's recommendation of documents
Input Types Self-Reports on Proficiencies and Interests Checking off of topics areas to be considered in publication recommendation
Inference Other Machine Learning Techniques Hebbian learning of U's preference weights


Pazzani, M. J., & Billsus, D. (1999). Evaluating adaptive web site agents. Proceedings of the Workshop on Recommender Systems Algorithms and Evaluation, 22nd International Conference on Research and Development in Information Retrieval.
Functions Help U to Find Information Recommend publications for downloading from a web site
Properties General Interests and Preferences Preferences concerning the criteria for S's recommendation of documents
Input Types Self-Reports on Proficiencies and Interests Checking off of topics areas to be considered in publication recommendation
Inference Other Machine Learning Techniques Hebbian learning of U's preference weights
Empirical Foundations Experience With Real-World Use Various other evaluations of real-world use


Peot, M., & Shachter, R. (1998). Learning from what you don't observe. In G. F. Cooper & S. Moral (Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference (pp. 439-446). San Francisco: Morgan Kaufmann. http://www2.sis.pitt.edu/~dsl/UAI/uai.html
Properties Personal Characteristics Symptoms (of users as patients)
Input Types Self-Reports on Personal Characteristics Answers to open questions about symptoms
Inference Decision-Theoretic Methods For interpretation of answers to open questions


Petrelli, D., De Angeli, A., & Convertino, G. (1999). A user centered approach to user modelling. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference (pp. 255-264). Vienna, New York: Springer Wien New York. http://www.cs.usask.ca/UM99/
Functions Tailor Information Presentation Serve as museum tour guide
Input Types Low-Level Measures of Context Position sensors
Empirical Foundations Early Exploratory Studies Requirements capture with a questionnaire


Picard, R. W. (1997). Affective computing. Cambridge,MA: MIT Press.
Input Types Low-Level Measures of Psychological States Vocal intonation, facial expression, various physiological indices
Inference Other Inference Methods Various approaches to pattern recognition, for emotions


Pohl, W. (1998). Logic-based representation and reasoning for user modeling shell systems. Sankt Augustin, Germany: infix.
General Works Overviews Concerning Particular Types of Inference Dissertation which includes thorough reviews of the uses of logic for user modeling


Pohl, W., & Höhle, J. (1997). Mechanisms for flexible representation and use of knowledge in user modeling shell systems. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 403-414). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Other Functions Support various types of adaptation with a general user modeling shell system
Inference Logic Various inference techniques within a modal logic framework


Pohl, W., & Nick, A. (1999). Machine learning and knowledge-based user modeling in the LaboUr approach. In J. Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International Conference (pp. 179-188). Vienna, New York: Springer Wien New York. http://www.cs.usask.ca/UM99/
General Works Overviews Concerning Particular Types of Inference Comparison of machine learning with other inference types; proposal of an integrative architecture


Quaresma, P., & Lopes, J. G. (1997). Modeling agents in dialogue systems. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 101-103). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Other Functions Take into account U's cooperativeness, sincerity, and credulity
Properties General Interests and Preferences Cooperativeness, sincerity, and credulity
Current Goal U's specific dialog goals in a dialog
Beliefs Dialog-relevant factual beliefs


Raskutti, B., & Zukerman, I. (1994). Query and response generation during information-seeking interactions. In A. Kobsa & D. Litman (Eds.), Proceedings of the Fourth International Conference on User Modeling (pp. 25-30). Boston, MA: User Modeling, Inc.
Functions Control Dialog Decide when and how to elicit further information from U


Rich, E. (1989). Stereotypes and user modeling. In A. Kobsa & W. Wahlster (Eds.), User models in dialog systems (pp. 35-51). Berlin: Springer.
General Works Overviews Concerning Particular Types of Inference Frequently cited exposition of the stereotype approach by its originator


Rogers, S., Fiechter, C., & Langley, P. (1999). An adaptive interactive agent for route advice. Proceedings of the Third International Conference on Autonomous Agents, Seattle, WA. http://riven.stanford.edu/~rogers/
Functions Recommend Products or Other Objects Recommend driving routes
Properties General Interests and Preferences Importance to U of various attributes of routes
Input Types Evaluations of Specific Objects Choices among suggested driving routes
Inference Neural Networks and Related Techniques  
Empirical Foundations Controlled Evaluations With Users Various other controlled evaluations


Sawhney, N., & Schmandt, C. (1999). Nomadic radio: Scaleable and contextual notification for wearable audio messaging. In M. G. Williams, M. W. Altom, K. Ehrlich, & W. Newman (Eds.), Human factors in computing systems: CHI '99 conference proceedings (pp. 96-103). New York: ACM. http://www.media.mit.edu/~nitin/NomadicRadio/
Functions Other Functions Tailor notifications to U' s current situation
Properties Context of Interaction Aspects of U's context that determine the desirability of interruptions
Input Types Naturally Occurring Actions Interaction with notification device
Low-Level Measures of Context Sensors for detecting speech in the environment
Inference Application-Specific Computations Formulas for determining desirability of notification given contextual cues
Empirical Foundations Informal Responses by Early Users A user's comments after a two-day trial


Schäfer, R., & Weyrath, T. (1997). Assessing temporally variable user properties with dynamic Bayesian networks. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 377-388). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Tailor Information Presentation Take into account U's available working memory capacity
Properties Proficiencies U's familiarity with emergency situations
Psychological States Available working memory capacity, emotional state, etc.
Input Types Naturally Occurring Actions Aspects of behavior that reflect available working memory capacity
Inference Decision-Theoretic Methods Dynamic Bayesian networks for inferences about unobservable temporally variable properties
Empirical Foundations Knowledge Acquisition Retrospective thinking-aloud study of inferences by firemen about emergency callers


Schafer, J. B., Konstan, J., & Riedl, J. (1999). Recommender systems in e-commerce. Proceedings of the ACM Conference on Electronic Commerce. http://www.cs.umn.edu/Research/GroupLens/
General Works Overviews Concerning Particular Types of Adaptation Overviews concerning commercially oriented personalized adaptive Web sites


Schaumburg, H. (2001). Computers as tools or as social actors? - the users' perspective on anthropomorphic agents. International Journal on Intelligent Cooperative Information Systems. http://www.cmr.fu-berlin.de/faculty/heike/publications.html
Empirical Foundations Experience With Real-World Use Various other evaluations of real-world use


Schiele, B., Starner, T., Rhodes, B., Clarkson, B., & Pentland, A. (2000). Situation aware computing with wearable computers. In W. Barfield & T. Caudell (Eds.), Augmented reality and wearable computers. Mahwah, NJ: Erlbaum. http://www.media.mit.edu/~bernt
Input Types Low-Level Measures of Context Use of miniature camera and/or microphone for coarse analysis of mobile user's context


Schmidt, A., Gellerson, H. W., & Beigl, M. (1999). A wearable context-awareness component: Finally a good reason to wear a tie. In C. Esposito & J. Siegel (Eds.), Proceedings of the Third International Symposium on Wearable Computers (pp. 176-177). Los Alamitos, CA: IEEE Computer.
Input Types Low-Level Measures of Context Use of miniature camera and/or microphone for coarse analysis of mobile user's context


Sears, A., & Shneiderman, B. (1994). Split menus: Effectively using selection frequency to organize menus. ACM Transactions on Computer-Human Interaction, 1, 27-51. http://www.cs.umd.edu/TRs/authors/Ben_Shneiderman.html
Functions Adapt an Interface Studies of adaptive menus
Empirical Foundations Controlled Evaluations With Users Various other controlled evaluations


Segal, R. B., & Kephart, J. O. (2000). Incremental learning in SwiftFile. In P. Langley (Ed.), Machine Learning: Proceedings of the 2000 International Conference. San Francisco: Morgan Kaufmann. http://www.research.ibm.com/swiftfile/
Functions Help U With Routine Tasks Help U to handle e-mail and perform other routine tasks
Inference Other Machine Learning Techniques Use of text classification methods to support filing of email


Segal, R. B., & Kephart, J. O. (1999). MailCat: An intelligent assistant for organizing e-mail. Proceedings of the Third International Conference on Autonomous Agents, pp. 276-282. http://www.research.ibm.com/swiftfile/
Functions Help U With Routine Tasks Help U to handle e-mail and perform other routine tasks
Inference Other Machine Learning Techniques Use of text classification methods to support filing of email


Seta, K., Ikeda, M., Kakusho, O., & Mizoguchi, R. (1997). Capturing a conceptual model for end-user programming: Task ontology as a static user model. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 203-214). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Other Functions Enable U to write and debug programs using high-level concepts that U finds natural
Properties Other Properties A task ontology that is suited to U's way of thinking
Empirical Foundations Informal Responses by Early Users Users' responses to a conceptual-level programming environment


Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating "word of mouth". In I. R. Katz, R. Mack, L. Marks, M. B. Rosson, & J. Nielsen (Eds.), Human factors in computing systems: CHI '95 conference proceedings (pp. 210-217). New York: ACM.
Functions Recommend Products or Other Objects Recommend entertainment objects (e.g., films)
Properties Other Properties Membership in a group of similar users
Inference Social Recommendation Comparison of several algorithms
Empirical Foundations Informal Responses by Early Users E-mailed comments of early users
Evaluations With an Empirical Database Empirical comparison of several collaborative filtering algorithms


Shneiderman, B., & Maes, P. (1997). Direct manipulation vs. interface agents. interactions, 4(6), 42-61.
General Works Other Overviews Lively debate about the virtues and limitations of interface agents, many of the arguments applying to user-adaptive systems


Sison, R., & Shimura, M. (1998). Student modeling and machine learning. International Journal of Artificial Intelligence in Education, 9, 128-158.
General Works Overviews Concerning Particular Types of Inference Overview of use of machine learning techniques for student modeling


Spooner, R. I., & Edwards, A. D. (1997). User modelling for error recovery: A spelling checker for dyslexic users. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 147-157). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Adapt an Interface Suggest corrections of (idiosyncratic) spelling errors of dyslexic users
Properties Beliefs Rules that underlie U's incorrect spelling behavior
Input Types Naturally Occurring Actions U's misspellings and ultimately chosen corrections
Inference Application-Specific Computations Updating of weights of incorrect spelling rules
Empirical Foundations Controlled Evaluations With Users Study of relationships among models of spelling behavior of different dyslexic writers


Staff, C. (1997). HyperContext: A model for adaptive hypertext. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 33-35). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Help U to Find Information Adapt hypertext links and recommendations to U's previous navigation behavior
Properties Current Goal Context of current hypertext node, which reflects U's interests
Input Types Self-Reports on Proficiencies and Interests Explicit selection of hypertext contexts
Naturally Occurring Actions Hypermedia pages that U has visited


Stein, A., Gulla, J. A., & Thiel, U. (1997). Making sense of users' mouse clicks: Abductive reasoning and conversational dialogue modeling. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 89-100). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Adapt an Interface Offer suitable next move after an unexpected, ambiguous dialog act
Input Types Naturally Occurring Actions History of dialog acts


Strachan, L., Anderson, J., Sneesby, M., & Evans, M. (1997). Pragmatic user modelling in a commercial software system. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 189-200). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Adapt an Interface Provide special support and interface simplifications for novice users
Properties Proficiencies Proficiency in the task domain and in the use of S
Input Types Self-Reports on Personal Characteristics Job title, level of education, etc.
Self-Reports on Proficiencies and Interests Self-assessments of domain and system competence
Naturally Occurring Actions Performance of particular tasks
Inference Application-Specific Computations Algorithm for updating assessments of system-related proficiency
Empirical Foundations Experience With Real-World Use Rating of adaptive and nonadaptive versions of a system by real users


Trewin, S., & Pain, H. (1997). Dynamic modelling of keyboard skills: Supporting users with motor disabilities. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 135-146). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Adapt an Interface Recommend keyboard adaptations for users with disabilities
Input Types Naturally Occurring Actions Aspects of disabled users' keyboard use
Empirical Foundations Controlled Evaluations With Users Assessment of the appropriateness of keyboard adaptation recommendations


André, E., Cohen, R., Graf, W., Kass, B., Paris, C., & Wahlster, W. (Eds.) (1992). Proceedings of the 1992 international workshop on user modeling. Saarbrücken, Germany: German Research Center for Artificial Intelligence (DFKI).
General Works Conference Proceedings on User Modeling This is the series of international conferences that brings together the broadest sample of research on user-adaptive systems, though some types of relevant research are still somewhat underrepresented


Kobsa, A., & Litman, D. (Eds.) (1994). Proceedings of the Fourth International Conference on User Modeling. Boston, MA: User Modeling, Inc.
General Works Conference Proceedings on User Modeling This is the series of international conferences that brings together the broadest sample of research on user-adaptive systems, though some types of relevant research are still somewhat underrepresented


Carberry, S., & Zukerman, I. (Eds.) (1996). Proceedings of the Fifth International Conference on User Modeling. Boston, MA: User Modeling, Inc.
General Works Conference Proceedings on User Modeling This is the series of international conferences that brings together the broadest sample of research on user-adaptive systems, though some types of relevant research are still somewhat underrepresented


Jameson, A., Paris, C., & Tasso, C. (Eds.) (1997). User modeling: Proceedings of the Sixth International Conference, UM97. Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
General Works Conference Proceedings on User Modeling This is the series of international conferences that brings together the broadest sample of research on user-adaptive systems, though some types of relevant research are still somewhat underrepresented


Kay, J. (Ed.) (1999). UM99, User modeling: Proceedings of the Seventh International Conference. Vienna, New York: Springer Wien New York. http://www.cs.usask.ca/UM99/
General Works Conference Proceedings on User Modeling This is the series of international conferences that brings together the broadest sample of research on user-adaptive systems, though some types of relevant research are still somewhat underrepresented


Dyke, N. W., Lieberman, H., & Maes, P. (1999). Butterfly: A conversation-finding agent for internet relay chat. In M. T. Maybury (Ed.), IUI99: International Conference on Intelligent User Interfaces (pp. 39-41). New York: ACM.
Functions Help U to Find Information Identify relevant chat groups
Properties General Interests and Preferences Interest in chat group topics


Vassileva, J. (1996). A task-centered approach for user modeling in a hypermedia office documentation system. User Modeling and User-Adapted Interaction, 6, 185-224.
Functions Help U to Find Information Help U find relevant task-oriented information in a hypermedia office documentation system
Properties Personal Characteristics U's role within her organization (which determines her tasks and priviledges)
Proficiencies U's level of domain expertise, both global and with regard to specific subtasks
Current Goal U's current information retrieval task
Input Types Self-Reports on Personal Characteristics Specification of user's own role in her organization
Self-Reports on Proficiencies and Interests Editing of expertise assessments in the user model
Other Self-Reports Explicit specification of current task
Naturally Occurring Actions Browsing behavior, as an indicator of expertise
Inference Application-Specific Computations Procedure for computing U's expertise on the basis of U's navigation actions
Application-Specific Rules Rules relating U's role in her organization to tasks and access privileges


Vassileva, J. (1997). A new view of interactive human-computer environments. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 433-435). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Other Functions Take into account factors such as U's relationship with S and the importance of U's goals.
Properties Psychological States U's goal priorities, emotions, moods, and relationship with S


Wahlster, W., & Kobsa, A. (1989). User models in dialog systems. In A. Kobsa & W. Wahlster (Eds.), User models in dialog systems (pp. 4-34). Berlin: Springer.
General Works Other Overviews An influential overview of 1980's research on user-adaptive dialog systems


Walker, M. A., Langkilde, I., Wright, J., Gorin, A., & Litman, D. J. (2000). Learning to predict problematic situations in a spoken dialogue system: Experiments with how may i help you? Proceedings of the First Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL'00), Seattle, WA, pp. 210-217.
Inference Rule Learning  


Wasfi, A. M. (1999). Collecting user access patterns for building user profiles and collaborative filtering. In M. T. Maybury (Ed.), IUI99: International Conference on Intelligent User Interfaces (pp. 57-64). New York: ACM.
Functions Help U to Find Information Recommendations of Web sites


Weber, G., & Specht, M. (1997). User modeling and adaptive navigation support in WWW-based tutoring systems. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 289-300). Vienna: Springer Wien New York. http://www.cs.uni-sb.de/UM97/
Functions Support Learning Guide U toward suitable instructional Web pages
Support Learning Present instructional examples from U's own learning history
Properties Beliefs Knowledge of particular concepts in instructional material
Input Types Responses to Test or Practice Items Answers to test items in a tutoring system
Naturally Occurring Actions Hypermedia pages that U has visited
Naturally Occurring Actions Previous handling of instructional examples by U
Inference Application-Specific Rules Principle for inferring knowledge of concepts that are prerequisites for known concepts
Empirical Foundations Controlled Evaluations With Users Study of effects of navigation support on students' motivation and the efficiency of their Web navigation


Weibelzahl, S. (1999). Conception, implementation, and evaluation of a case-based system for sales support in the internet. Department of Psychology, University of Trier\CommaGermany/. http://www.cs.uni-sb.de/users/jameson/pdf/weibelzahl.pdf
Functions Recommend Products or Other Objects Recommend vacation houses
Properties General Interests and Preferences U's needs and goals concerning a vacation house
Input Types Self-Reports on Proficiencies and Interests Specifcation of desires concerning a vacation house
Inference Instance-Based Learning Case-based learning for product recommendation
Empirical Foundations Controlled Evaluations With Users Various other controlled evaluations
Experience With Real-World Use Various other evaluations of real-world use