Personalized Web Documents Filtering by Analyzing User Browsing Behaviors
Essam Natsheh
International Journal of Information Studies, Vol. 5, No. 2, 2000.
DOI: 
Link: https://www.dline.info/ijis/fulltext/v5n2/ijisv5n2_2.pdf
Abstract:
This paper describes a method for information filtering in web browsing to learn userâ€™s preferences. The
purposed method observes userâ€™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 userâ€™s interests. In contrast to conventional
relevance feedback methods which require explicit user feedbacks, our approach learns user preference implicitly from
direct observations of browsing behavior during interaction. We made field tests that involved number of users and number
of document during period of time, to show if our methods have the superior performance in personalized information
filtering.