Learning User Interest Dynamics with a Three-Descriptor Representation
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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.
Journal of the American Society for Information Science and Technology
author list (cited authors)
Widyantoro, D. H., Ioerger, T. R., & Yen, J.
complete list of authors
Widyantoro, DH||Ioerger, TR||Yen, J