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APWEB
2006
Springer

Adaptive User Profile Model and Collaborative Filtering for Personalized News

9 years 3 months ago
Adaptive User Profile Model and Collaborative Filtering for Personalized News
Abstract. In recent years, personalized news recommendation has received increasing attention in IR community. The core problem of personalized recommendation is to model and track users' interests and their changes. To address this problem, both content-based filtering (CBF) and collaborative filtering (CF) have been explored. User interests involve interests on fixed categories and dynamic events, yet in current CBF approaches, there is a lack of ability to model user's interests at the event level. In this paper, we propose a novel approach to user profile modeling. In this model, user's interests are modeled by a multi-layer tree with a dynamically changeable structure, the top layers of which are used to model user interests on fixed categories, and the bottom layers are for dynamic events. Thus, this model can track the user's reading behaviors on both fixed categories and dynamic events, and consequently capture the interest changes. A modified CF algorithm b...
Jue Wang, Zhiwei Li, Jinyi Yao, Zengqi Sun, Mingji
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where APWEB
Authors Jue Wang, Zhiwei Li, Jinyi Yao, Zengqi Sun, Mingjing Li, Wei-Ying Ma
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