Personalization Recommendation Algorithm Based on Trust Correlation Degree and Matrix Factorization

Weimin Li, Xiaokang Zhou, Shohei Shimizu, Mingjun Xin, Jiulei Jiang, Honghao Gao, Qun Jin

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The rapid development of the Internet of Things (IoT) and e-commerce has brought a lot of convenience to people's lives. IoT applications generate a large number of services and user data. It is necessary to design a personalized recommendation technology suitable for the users of IoT services and improve the user experience. In this paper, a recommendation algorithm with trusted relevance combined with matrix factorization is proposed. By establishing an effective trust metric model, the user's social information is integrated into the recommendation algorithm. First, the social network concentric hierarchical model is used to consider the direct or indirect trust relationship, and more trust information is integrated for the matrix factorization recommendation algorithm. Then, we design the trust relevance, comprehensively considering the trust factors and interest similar factors. Our experiments were performed on the Dianping datasets. The recommendation algorithm using matrix factorization and trusted relevance degree has higher prediction accuracy than the basic matrix decomposition and social matrix factorization in terms of accuracy and stability.

Original languageEnglish
Article number8598773
Pages (from-to)45451-45459
Number of pages9
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019 Jan 1

Keywords

  • collaborative filtering
  • IoT
  • matrix factorization
  • personalized recommendation
  • trust

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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