Interest Mining and Recommender System for Local Activities
Cheng Guangyao · 2008
Local activities become the most popular application of SNS (social networking services). Due to the exponential increase of activities information and the urgent need for ways to cope with it, this paper describes the design and implementation of an interest mining and recommender systems. The user interest model is a major factor in the efficiency of the recommender system, thus modeling according to the userspsila interests is one of the problems which should be a major concern in personalized service. The system adopts interest mining technology and can automatically identify the userpsilas interests and recommend interest-related local activities initiatively. This paper improves a category-based collaborative filtering algorithm base on authority of user. Experimental results show that this method can overcome the problem of extreme sparsity in collaborative filtering, and provide better recommendation results than traditional collaborative filtering algorithm.