Personalization for the Web: Learning User Preferences from Text

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 naive Bayes method. Experiments conducted in the context of a content-based profiling system for movies show the pros and cons of each method.