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Improved Recommendations Based on Trust Relationships in Social Networks

1
School of Information Engineering, Hubei University of Economics, No. 8, Yangqiaohu Ave., Jiangxia Dist., Wuhan 430205, China
2
Graduate School of Information, Production and Systems, Waseda University, Hibikino 2–7, Wakamatsu-ku, Kitakyushu 808-0135, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Emilio Ferrara
Future Internet 2017, 9(1), 9; https://doi.org/10.3390/fi9010009
Received: 24 February 2017 / Revised: 15 March 2017 / Accepted: 17 March 2017 / Published: 21 March 2017
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. View Full-Text
Keywords: recommendation; trust relationship; accuracy; cold start; sparsity recommendation; trust relationship; accuracy; cold start; sparsity
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MDPI and ACS Style

Tian, H.; Liang, P. Improved Recommendations Based on Trust Relationships in Social Networks. Future Internet 2017, 9, 9.

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