Social recommendation based on trust and influence in SNS environments

Weimin Li, Zhengbo Ye, Minjun Xin, Qun Jin

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)11585-11602
Number of pages18
JournalMultimedia Tools and Applications
Volume76
Issue number9
DOIs
Publication statusPublished - 2017 May 1

Keywords

  • Recommendation algorithm
  • Similarity
  • Social influence
  • Trust

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

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