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.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
  H.3 INFORMATION STORAGE AND RETRIEVAL

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Intelligent agents

General Terms:
Algorithms, Design, Experimentation, Human Factors, Management, Measurement, Performance, Reliability, Theory

Keywords:
information filtering, intelligent agents