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Personalized Recommendation Leveraging Social Relationship

  • Wenchao Shi
  • Yiguang Lu
  • Zhipeng Huang
  • Enbo Du
  • Yihan Cheng
  • Jinpeng ChenEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

The goal of recommender system is recommending products users may be interested in. Recommending content is an important task in many platforms. But many platforms only have implicit feedback information from users. Each single user generates behavior on only a few products lead to sparse datasets from users. And traditional recommending algorithms show poor performance when face the two problems. In this paper, we present a social bayesian personalized ranking based on similar friends (SFSBPR). We alleviate the sparsity problem when recommend by users’ implicit feedback. Experiment on real-world sparse datasets show that our method outperforms current state-of-the-art recommendation method.

Keywords

Personalized recommendation Social relationship Epinions SVD 

Notes

Acknowledgement

This work is supported by the Fundamental Research Funds for the Central Universities under Grant No. 2017RC55, the National Natural Science Foundation of China under Grant No. 61702043, No. 61502202, the Education Teaching Reform Project of BUPT in 2018 under Grant No. 2018JY-B04, and Research Innovation Fund for College Students of BUPT.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Wenchao Shi
    • 1
  • Yiguang Lu
    • 1
  • Zhipeng Huang
    • 1
  • Enbo Du
    • 1
  • Yihan Cheng
    • 1
  • Jinpeng Chen
    • 1
    Email author
  1. 1.School of Software EngineeringBeijing University of Posts and TelecommunicationsBeijingChina

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