This document contains references from various papers and books on topics related to user modeling, information retrieval, recommender systems, and machine learning. Specifically, it lists over 30 references used in research on statistical machine learning, collaborative filtering algorithms, information needs modeling, language modeling, text clustering, and other areas related to adaptive information access and recommendation. The references are from a variety of published sources including conference proceedings, journals, books, and theses spanning the years 1979-2003.

1. AntWebBib_Full
References
Berger, A. (2001) Statistical Machine Learning for information retrieval, PhD dissertation, Department of
Computer Science, Carnegie Mellon University, 2001.
Breese J.S., Heckerman D. and Kadie C. (1998), Empirical Analysis of Predictive Algorithms for
Collaborative Filtering, Proceedings 14th Conference on Uncertainty in Artificial Intelligence, Madison
WI: Morgan Kauffman.
Campbell, I. and van Rijsbergen, K. (1996) The Ostensive Model of developing information needs,
CoLIS-2, Copenhagen.
Furnas, G. W. and Landauer, T. K. and Gomez, L. M. and Dumais, S. T. (1983) Statistical Semantics:
Analysis of the Potential Performance of Key-Word Information Systems, The Bell System Technical
Journal, 62(6), 1753-1806.
He, D. and Goker, A. and Harper, D. J. (2002) Combining Evidence for Automatic Web Session
Identification, Processing and Management, 38(5): 727-742, 2002.
Hearst, M. and Pedersen, P. (1996) Reexamining the Cluster Hypothesis: Scatter/Gather on Retrieval
Results, SIGIR 1996, Zurich.
Kelly, D. and Teevan, J. (2003). Implicit feedback for inferring user preference: A bibliography. SIGIR
Forum, 37(2), 18-28.
Kosmynin, A. (1996) An Information Broker for Adaptive Distributed Resource Discovery Service,
Proceedings of WebNet ’96, San Francisco.
Lavrenko, V. and Croft, W. B. (2001) Relevance based language models, SIGIR 2001, New Orleans,
120-127.
Jurafsky, D. and Martin, J. H. (2000) Speech and Language Processing – An Introduction to Natural
Language Processing, Computational Linguistics, and Speech Recognition, Prentice-Hall.
Manning, C. and Schutze, H. (2001) Foundations of Natural Language Processing, The MIT Press.
Mukhopadhyay, S., Peng, S., Raje, R., Palakal, M., & Mostafa, J. "Large-scale Multi-agent Information
Classification Using Dynamic Acquaintance Lists". Journal of the American Society for Information
Science & Technology, 54(10), 2003.
Muresan, G. (2002) Language Modelling and Document Clustering for Mediated Information Retrieval,
PhD thesis, The Robert Gordon University, UK.
Muresan, G. and Harper, D. J. (2001) Document Clustering and Language Models for System-Mediated
Information Access in Proceedings of the 5th European Conference on Research and Advanced
Technology for Digital Libraries, Darmstadt, 4-9 September 2001, pp.438-449.
Ponte, J. M. and Croft, W. B. (1998) Language modeling for Information Retrieval, SIGIR 1998,
Melbourne, 275-281.
Shardanand U. and Maes (1995), Social information filtering: Algorithms for automating "word of mouth",
Proceedings of CHI'95 -- Human Factors in Computing Systems, 210-217.

2. Willett, P. (1988) Recent Trends in Hierarchic Document Clustering. A Critical Review”, Information
Processing and Management, 24, 577-597.
Witten, I. H. and Moffat, A. and Bell, T. C. (1999) Managing Gigabytes. Compressing and Indexing
Documents and Images, Morgan Kaufmann.
Xu, J. and Croft, W. B. (1999) Cluster-based language models for distributed retrieval, SIGIR’99,
Berkeley, p.254-261.
References
Bushey, R., Manuney, J.M., & Deelman T. (1999). The development of behavior-based
user models for a computer system, In Proceedings of the Seventh International
Conference on User Modeling, pp.109-118.
Kuflik, T., Shapira, B. & Shoval, P. (2003).Stereotype-based versus personal-based
filtering rules in information filtering systems. Journal of the American Society
for Infomation Science and Technology, 54(3):243-250.
Mitchell, K., Woodbury, M.A. & Norcio, A.F. (1994). Individualizing user interfaces:
Application of the grade of membership (GoM) model for development of fuzzy
user classes. Information Science, 1:9-29
Middleton, S. E., Shadbolt, N. R. and De Roure, D. C. (2004). Ontological user profiling
in recommender systems. ACM Transactions on Information Systems, 22(1): 54–
88.
Motaner, M., Lopez, B. & J. Lluis De La (2003). A taxonomy of recommender agents on
the Internet. Artificial Intelligence Review, 19: 285-330.
Shapira, B., Shoval, P. & Hanani, U. (1997). Stereotypes in information filtering systems.
Information Processing and Management, 33(3):273-287.
Shapira, B., Shoval, P. & Hanani, U. (1999). Experimentation with an information
filtering system that combines cognitive and sociological filtering integrated with
user stereotypes. Decision Support Systems 27, pp. 5-24.
Rich, E. (1979). Users modeling via stereotypes. Cognitive Science, 3: 329-354.
Rich, E. (1989). Stereotypes and user modeling. In A. Kobsa, W. Wahlster (Eds.): User
Models in Dialog Systems, Springer-Verlag, Berlin. pp.35-51.
Zhang, X. (2003). Discriminant Analysis as a Machine Learning Method for Revision of User Stereotypes
of Information Retrieval Systems. In Sofus A. Macskassy, et al (Eds.): In Workshop Proceedings of
Machine Learning, Information Retrieval and User