Learning User Interest Dynamics with a Three-Descriptor Representation

Dwi H. Widyantoro, Thomas R. Ioerger, John Yen

Research output: Contribution to journalArticlepeer-review

91 Scopus citations


Learning users' interest categories is challenging in a dynamic environment like the Web because they change over time. This article describes a novel scheme to represent a user's interest categories, and an adaptive algorithm to learn the dynamics of the user's interests through positive and negative relevance feedback. We propose a three-descriptor model to represent a user's interests. The proposed model maintains a long-term interest descriptor to capture the user's general interests and a short-term interest descriptor to keep track of the user's more recent, faster-changing interests. An algorithm based on the three-descriptor representation is developed to acquire high accuracy of recognition for long-term interests, and to adapt quickly to changing interests in the short-term. The model is also extended to multiple three-descriptor representations to capture a broader range of interests. Empirical studies confirm the effectiveness of this scheme to accurately model a user's interests and to adapt appropriately to various levels of changes in the user's interests.

Original languageEnglish (US)
Pages (from-to)212-225
Number of pages14
JournalJournal of the American Society for Information Science and Technology
Issue number3
StatePublished - Feb 1 2001

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence


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