Interest Mining and Recommender System for Local Activities

Authors:
Cheng Guangyao
Affiliations:
-
Venue:
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Year:
2008

Citing 3
Cited 0

Collaborative filtering via gaussian probabilistic latent semantic analysis

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Evaluating collaborative filtering recommender systems

ACM Transactions on Information Systems (TOIS)
Consumers' Interest in Personalized Recommendations: The Role of Product-Involvement and Opinion Seeking

HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences

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Abstract

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 users’ 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 user’s 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.