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SOCIAL INTEREST FOR USER SELECTING ITEMS IN RECOMMENDER SYSTEMS

Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China

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MING-JING DING

Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China

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YAN FU

Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China

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JUN-LIN ZHOU

Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China

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, and

ZI-KE ZHANG

Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China

Institute of Information Economy, Hangzhou Normal University, Hangzhou 310036, P. R. China

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https://doi.org/10.1142/S0129183113500228Cited by:8 (Source: Crossref)

Abstract

Recommender systems have developed rapidly and successfully. The system aims to help users find relevant items from a potentially overwhelming set of choices. However, most of the existing recommender algorithms focused on the traditional user-item similarity computation, other than incorporating the social interests into the recommender systems. As we know, each user has their own preference field, they may influence their friends' preference in their expert field when considering the social interest on their friends' item collecting. In order to model this social interest, in this paper, we proposed a simple method to compute users' social interest on the specific items in the recommender systems, and then integrate this social interest with similarity preference. The experimental results on two real-world datasets Epinions and Friendfeed show that this method can significantly improve not only the algorithmic precision-accuracy but also the diversity-accuracy.

Keywords:

PACS: 89.75.Hc, 87.23.Ge, 05.70.Ln

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