Learning user's preferences by analyzing Web-browsing behaviors

Abstract

This paper describes a method for an information filtering agent to learn user's preferences. The proposed method observes user's reactions to the filtered documents and learns from them the profiles for the individual users. Reinforcement learning is used to adapt the most significant terms that best represent user's interests. In contrast to conventional relevance feedback methods which require explicit user feedbacks, our approach learns user preferences implicitly from direct observations of browsing behaviors during interaction. Field tests have been made which involved 10 users reading a total of 18,750 HTML documents during 45 days. The proposed method showed superior performance in personalized information filtering compared to the existing relevance feedback methods.