A reinforcement learning agent for personalized information filtering

摘要

This paper describes a method for learning user's interests in the Web-based personalized information filtering system called WAIR. The proposed method analyzes user's reactions to the presented documents and learns from them the profiles for the individual users. Reinforcement learning is used to adapt the term weights in the user profile so that user's preferences are best represented. In contrast to conventional relevance feedback methods which require explicit user feedbacks, our approach learns user preferences implicitly from direct observations of user behaviors during interaction. Field tests have been made which involved 7 users reading a total of 7,700 HTML documents during 4 weeks. The proposed method showed superior performance in personalized information filtering compared to the existing relevance feedback methods.