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Social recommendation based on trust and influence in SNS environments

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Abstract

The development of social media provides convenience to people’s lives. People’s social relationship and influence on each other is an important factor in a variety of social activities. It is obviously important for the recommendation, while social relationship and user influence are rarely taken into account in traditional recommendation algorithms. In this paper, we propose a new approach to personalized recommendation on social media in order to make use of such a kind of information, and introduce and define a set of new measures to evaluate trust and influence based on users’ social relationship and rating information. We develop a social recommendation algorithm based on modeling of users’ social trust and influence combined with collaborative filtering. The optimal linear relation between them will be reached by the proposed method, because the importance of users’ social trust and influence varies with the data. Our experimental results show that the proposed algorithm outperforms traditional recommendation in terms of recommendation accuracy and stability.

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Acknowledgments

This work was partly supported by National Natural Science Foundation under Grant No. 61074315 and No. 71061005/G0112, and Natural Science Foundation of Ningxia under Grant No. NZ12212.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, China

    Weimin Li, Zhengbo Ye & Minjun Xin

  2. College of Information Engineering, China Jiliang University, Hangzhou, China

    Qun Jin

  3. Faculty of Human Sciences, Waseda University, Tokorozawa, Japan

    Qun Jin

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  1. Weimin Li
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  2. Zhengbo Ye
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  3. Minjun Xin
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  4. Qun Jin
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Correspondence to Qun Jin.

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Li, W., Ye, Z., Xin, M. et al. Social recommendation based on trust and influence in SNS environments. Multimed Tools Appl 76, 11585–11602 (2017). https://doi.org/10.1007/s11042-015-2732-0

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  • DOI: https://doi.org/10.1007/s11042-015-2732-0

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