Recommender systems based on social networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

75 Scopus Citations
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Author(s)

  • Zhoubao Sun
  • Lixin Han
  • Wenliang Huang
  • Xueting Wang
  • Xiaoqin Zeng
  • Min Wang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)109-119
Journal / PublicationJournal of Systems and Software
Volume99
Online published5 Oct 2014
Publication statusPublished - Jan 2015

Abstract

The traditional recommender systems, especially the collaborative filtering recommender systems, have been studied by many researchers in the past decade. However, they ignore the social relationships among users. In fact, these relationships can improve the accuracy of recommendation. In recent years, the study of social-based recommender systems has become an active research topic. In this paper, we propose a social regularization approach that incorporates social network information to benefit recommender systems. Both users' friendships and rating records (tags) are employed to predict the missing values (tags) in the user-item matrix. Especially, we use a biclustering algorithm to identify the most suitable group of friends for generating different final recommendations. Empirical analyses on real datasets show that the proposed approach achieves superior performance to existing approaches.

Research Area(s)

  • Recommender system, Social network, Social-based recommender system

Citation Format(s)

Recommender systems based on social networks. / Sun, Zhoubao; Han, Lixin; Huang, Wenliang; Wang, Xueting; Zeng, Xiaoqin; Wang, Min; Yan, Hong.

In: Journal of Systems and Software, Vol. 99, 01.2015, p. 109-119.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal