Improved Recommendations Based on Trust Relationships in Social Networks

Hao Tian and Peifeng Liang
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Hao Tian: School of Information Engineering, Hubei University of Economics, No. 8, Yangqiaohu Ave., Jiangxia Dist., Wuhan 430205, China
Peifeng Liang: Graduate School of Information, Production and Systems, Waseda University, Hibikino 2–7, Wakamatsu-ku, Kitakyushu 808-0135, Japan

Future Internet, 2017, vol. 9, issue 1, 1-9

Abstract: In order to alleviate the pressure of information overload and enhance consumer satisfaction, personalization recommendation has become increasingly popular in recent years. As a result, various approaches for recommendation have been proposed in the past few years. However, traditional recommendation methods are still troubled with typical issues such as cold start, sparsity, and low accuracy. To address these problems, this paper proposed an improved recommendation method based on trust relationships in social networks to improve the performance of recommendations. In particular, we define trust relationship afresh and consider several representative factors in the formalization of trust relationships. To verify the proposed approach comprehensively, this paper conducted experiments in three ways. The experimental results show that our proposed approach leads to a substantial increase in prediction accuracy and is very helpful in dealing with cold start and sparsity.

Keywords: recommendation; trust relationship; accuracy; cold start; sparsity (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2017
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