Learning User Interest Dynamics with a Three-Descriptor Representation

Learning usersʹ 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 userʹs interest categories, and an adaptive algorithm to learn the dynamics of the userʹs interests through positive and negative relevance feedback. We propose a three-descriptor model to represent a userʹs interests. The proposed model maintains a long-term interest descriptor to capture the userʹs general interests and a short-term interest descriptor to keep track of the userʹs 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 userʹs interests and to adapt appropriately to various levels of changes in the userʹs interests.