An adaptive algorithm for learning changes in user interests

Abstract

In this paper, we describe a new scheme to learn dynamic user's interests in an automated information filtering and gathering system running on the Internet. Our scheme is aimed to handle multiple domains of long-term and short-term user's interests simultaneously, which is learned through positive and negative user's relevance feedback. We developed a 3-descriptor approach to represent the user's interest categories. Using a learning algorithm derived for this representation, our scheme adapts quickly to significant changes in user interest, and is also able to learn exceptions to interest categories.