Learning User Interest Dynamics with a Three-Descriptor Representation Academic Article uri icon


  • 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.

published proceedings

  • Journal of the American Society for Information Science and Technology

altmetric score

  • 3

author list (cited authors)

  • Widyantoro, D. H., Ioerger, T. R., & Yen, J.

citation count

  • 66

complete list of authors

  • Widyantoro, DH||Ioerger, TR||Yen, J

publication date

  • February 2001