Abstract
As more information becomes available electronically, tools for finding information of interest to users become increasingly important. Information preferences vary greatly across users, therefore, filtering systems must be highly personalized to serve the individual interests of the user. Our research deals with learning approaches to build user profiles that accurately capture user interests from content (documents) and that could be used for personalized information filtering. The learning mechanisms analyzed in this paper are relevance feedback and a naïve Bayes method. Experiments conducted in the context of a content-based profiling system for movies show the pros and cons of each method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rocchio, J.: Relevance feedback information retrieval. In: Salton, G. (ed.) The SMART retrieval system - experiments in automated document processing, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)
Mladenic, D.: Text-learning and related intelligent agents: a survey. IEEE Intelligent Systems 14, 44–54 (1999)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the 5th ACM Conference on Digital Libraries, pp. 195–204. ACM Press, New York (2000)
Pazzani, M., Billsus, D.: Learning and revising user profiles: The identification of interesting web sites. Machine Learning 27, 313–331 (1997)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34 (2002)
Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Yao, Y.Y.: Measuring retrieval effectiveness based on user preference of documents. Journal of the American Society for Information Science 46, 133–145 (1995)
Orkin, M., Drogin, R.: Vital Statistics. McGraw-Hill, New York (1990)
Miller, G.: WordNet: an online lexical database. International Journal of Lexicography 3 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Semeraro, G., Lops, P., Degemmis, M. (2005). Personalization for the Web: Learning User Preferences from Text. In: Hemmje, M., Niederée, C., Risse, T. (eds) From Integrated Publication and Information Systems to Information and Knowledge Environments. Lecture Notes in Computer Science, vol 3379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31842-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-31842-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24551-3
Online ISBN: 978-3-540-31842-2
eBook Packages: Computer ScienceComputer Science (R0)