Trust-based Collaborative Filtering

摘要：

Collaborative Filtering is one of the most successful techniques of Recommender Systems. Despite its success, similarity-based Collaborative Filtering methods suffer from inherent weakness: users tend to rate few items. As a result, the similarity is not easily computed. This paper aims to solve the above problem by introducing the trust metric into Collaborative Filtering. We develop a novel computation model of trust by incorporating the tastes of users. Then we propagate trust throughout the trust relationship network, and more potential neighbors can be found. At last, we make recommendations based on trust-based Collaborative Filtering. Experimental results on a real extremely sparse dataset have shown best performance of our method in terms of MAE and Coverage when compared with similarity-based Collaborative Filtering methods.

关键词： Recommender Systems Collaborative Filtering Trust Tastes

作者: Jing Wang Jian Yin Yuzhang Liu Chuangguang Huang

作者单位: School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China Neusof School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China School of Software,Sun Yat-sen University,Guangzhou 510006, China

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 2710-2714

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）