Abstract
We present a framework for adaptive news access, based on machine learning techniques specifically designed for this task. First, we focus on the system's general functionality and system architecture. We then describe the interface and design of two deployed news agents that are part of the described architecture. While the first agent provides personalized news through a web-based interface, the second system is geared towards wireless information devices such as PDAs (personal digital assistants) and cell phones. Based on implicit and explicit user feedback, our agents use a machine learning algorithm to induce individual user models. Motivated by general shortcomings of other user modeling systems for Information Retrieval applications, as well as the specific requirements of news classification, we propose the induction of hybrid user models that consist of separate models for short-term and long-term interests. Furthermore, we illustrate how the described algorithm can be used to address an important issue that has thus far received little attention in the Information Retrieval community: a user's information need changes as a direct result of interaction with information. We empirically evaluate the system's performance based on data collected from regular system users. The goal of the evaluation is not only to understand the performance contributions of the algorithm's individual components, but also to assess the overall utility of the proposed user modeling techniques from a user perspective. Our results provide empirical evidence for the utility of the hybrid user model, and suggest that effective personalization can be achieved without requiring any extra effort from the user.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Allan, J., Carbonell, J., Doddington, G., Yamron, J. and Yang, Y.: 1998, Topic detection and tracking pilot study final report. Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, 194-218, Lansdowne, VA.
Balabanovic, M.: 1998, Learning to surf: multiagent systems for adaptive web page recommendation. Ph.D. Thesis, Stanford University.
Bauer, M., Gmytrasiewicz, P. and Pohl, W.: 1999, Machine learning for user modeling, Seventh International Conference on User Modeling, Banff, Canada.
Belkin, N.: 1997, User modeling in information retrieval. [online]. Available: http://www.scils.rutgers.edu/~belkin/um97oh/. (June 7, 2000).
Billsus, D. and Pazzani, M.: 1999a, A personal news agent that talks, learns and explains, Proceedings of the Third International Conference on Autonomous Agents, Seattle, WA, pp. 268-275.
Billsus, D. and Pazzani, M.: 1999b, A hybrid user model for news story classification, User Modeling: Proceedings of the Seventh International Conference (UM99), Banff, Canada, pp. 98-108.
Chiu, B. and Webb, G.: 1998, Using decision trees for agent modeling: improving prediction performance. User Modeling and User-Adapted Interaction, 8, 131-152.
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D. and Sartin, M.: 1999, Combining content-based and collaborative filters in an online newspaper. ACMSIGIR Workshop on Recommender Systems, Berkeley, CA.
Cohen, W. and Hirsh, H.: 1998, Joins that generalize: text classi¢cation using WHIRL, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, NY, pp. 169-173.
Dietterich, T.: 1998, Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7), 1895-1924.
Duda, R. and Hart, P.: 1973, Pattern Classification and Scene Analysis, New York, NY: Wiley.
Jameson, A., Paris, C. and Tasso, C. (eds.): 1997, User Modeling: Proceedings of the Sixth International Conference (UM97), New York: Springer.
Joachims, T., McCallum, A., Sahami, M. and Ungar, L. (eds.): 1999, IJCAI Workshop IRF2: Machine Learning for Information Filtering, Stockholm, Sweden.
Kay, J. (ed.).: 1999, User Modeling: Proceedings of the Seventh International Conference (UM99), Banff, Canada.
Klinkenberg, R. and Renz, I.: 1998, Adaptive information filtering: learning in the presence of concept drift, AAAI/ICML-8Workshop on Learning for Text Categorization, Technical Report WS-98-05, Madison, WI.
Lang, K.: 1995, NewsWeeder: learning to filter news, Proceedings of the Twelfth International Machine Learning Conference (ICML '95), Lake Tahoe, CA, pp. 331-339.
Lewis, D. and Gale, W.A.: 1994, A sequential algorithm for training text classifiers, Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, pp. 3-12.
Lieberman, H.: 1995, Letizia: An agent that assists web browsing, Proceedings of the International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 924-929.
McCallum, A. and Nigam, K.: 1998, A comparison of event models for naive bayes text classification, AAAI/ICML-98Workshop on Learning for Text Categorization, Technical Report WS-98-05, AAAI Press, pp. 41-48.
Mooney, R., Bennet, P. and Roy, L.: 1998, Book recommending using text categorization with extracted information. AAAI/ICML-98Workshop on Learning for Text Categorization, Technical Report WS-98-05, AAAI Press, pp. 49-54.
Papatheodorou, C. (ed.).: 1999, Machine learning and applications workshop. Machine Learning in User Modeling, Chania, Greece.
Pazzani, M. and Billsus, D.: 1997, Learning and revising user profiles: the identification of interesting web sites. Machine Learning, 27, 313-331.
Quinlan, J.: 1986, Induction of decision trees. Machine Learning, 1, 81-106.
Rocchio, J. (1971). Relevance feedback in information retrieval, In: G. Salton (ed.). The SMART System: Experiments in Automatic Document Processing, NJ: Prentice Hall, pp. 313-323.
Rudstrom, A., Bauer, M., Iba, W. and Pohl, W. (eds.).: 1999, IJCAI Workshop ML4: Learning About Users, Stockholm, Sweden.
Sakagami, H. and Kamba, T.: 1997, Learning personal preferences on online newspaper articles from user behaviors. Proceedings of the Sixth International World Wide Web Conference (WWW6), Santa Clara, CA, pp. 291-300.
Salton, G.: 1989, Automatic Text Processing, Addison-Wesley.
Shardanand, U. and Maes, P.: 1995, Social information filtering: algorithms for automating `word of mouth', Proceedings of the Conference on Human Factors in Computing Systems (CHI95), Denver, CO, pp. 210-217.
Veltman, G.: 1998, A multi-agent system for generating a personalized newspaper digest. AAAI/ICML-98 Workshop on Learning for Text Categorization, Technical Report WS-98-05, AAAI Press, pp. 99-102.
Webb, G., Chiu, C. and Kuzmycz, M.: 1997, Comparative evaluation of alternative induction engines for feature based modeling. International Journal of Arti¢cial Intelligence in Education, 8, 97-115.
Webb, G. and Kuzmycz, M.: 1996, Feature based modeling: a methodology for producing coherent, consistent, dynamically changing models of agents' competencies. User Modeling and User Assisted Interaction, 5(2), 117-150.
Widmer, G. and Kubat, M.: 1996, Learning in the presence of concept drift and hidden contexts. Machine Learning, 23, 69-101.
Yang, Y.: 1999, An evaluation of statistical approaches to text categorization. Information Retrieval, 1(1), 67-88.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Billsus, D., Pazzani, M.J. User Modeling for Adaptive News Access. User Modeling and User-Adapted Interaction 10, 147–180 (2000). https://doi.org/10.1023/A:1026501525781
Issue Date:
DOI: https://doi.org/10.1023/A:1026501525781