Learning user interest dynamics with a three-descriptor representation

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 algorith m to learn the dynamics of the user's interests through positive and negati ve relevance feedback. We propose a three-descriptor model to represent a u ser's interests, The proposed model maintains a long-term interest descript or to capture the user's general interests and a short-term interest descri ptor to keep track of the user's more recent, faster-changing interests. An algorithm based on the three-descriptor representation is developed to acq uire high accuracy of recognition for long-term interests, and to adapt qui ckly to changing interests in the short-term, The model is also extended to multiple three-descriptor representations to capture a broader range of in terests. Empirical studies confirm the effectiveness of this scheme to accu rately model a user's interests and to adapt appropriately to various level s of changes in the user's interests.