Affiliations: [a] Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Yunnan, Kumming, P.R. China | [b] R&D Department, Qingqi Peugeot motorcycle co. Ltd., Jinan city, Shandong Province, China
Correspondence:
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Corresponding author. Weijiang Li, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Yunnan, Kumming 650500, P.R. China. Tel./Fax: +86 63340576; E-mail: hrbrichard@126.com.
Abstract: Nowadays, we are living in an information overload age. A tremendous amount of information has been produced on the Internet, how to find the interesting information is the main goal of recommendation system research. However, the most of current traditional recommendation algorithms (such as Collaborative filtering) are suffering from flowing difficulties: (i) The traditional recommendation system assume that users are independent and identically distributed; this assumption fails to consider the social relation and connection between users, which is not consistent with the social relations in our real world. (ii) Although there are some recommendation system research began to focus on the trust relationship between users, trust information is also very sparse. This leads to most of datasets only contains very little information about the user’s relationship. In this paper, we propose an innovative method that integrated users’ trust propagation and singular value decomposition into recommendation Algorithm to improve the quality of the recommendation effectively and efficiently. We performed our experiments on two real data sets respectively, the public domain Epinions.com and Filemtrust.com. The experimental results show that our method has a better outperform.
Keywords: Recommendation system, trust propagation, singular value decomposition
