Augmenting collaborative recommender by fusing explicit social relationships

Quan Yuan, Shiwan Zhao, Li CHEN, Yan Liu, Shengchao Ding, Xiatian Zhang, Wentao Zheng

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

13 Citations (Scopus)

Abstract

Nowadays social websites have become a major trend in the Web 2.0 environment, enabling abundant social data available. In this paper, we explore the role of two types of social relationships: membership and friendship, while being fused with traditional CF (Collaborative Filtering) recommender methods in order to more accurately predict users' interests and produce recommendations to them. Through an exploratory evaluation with real-life dataset from Last.fm, we have revealed respective effects of the two explicit relationships and furthermore their combinative impacts. In addition, the fusion is conducted via random walk graph model in comparison with via weighted neighborhood similarity matrix, so as to identify the best performance platform. Indepth analysis on the experimental data particularly shows the significant improvement by up to 8% on recommendation accuracy, by embedding social relationships in CF via graph model.

Original languageEnglish
Title of host publicationProceedings of the ACM RecSys'09 Workshop on Recommender Systems & the Social Web
Pages49-56
Number of pages8
Publication statusPublished - Oct 2009
EventWorkshop on Recommender Systems and the Social Web, Collocated with the 3rd ACM Conference on Recommender Systems, ACM RecSys 2009 - New York, NY, United States
Duration: 25 Oct 200925 Oct 2009

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS
Volume532
ISSN (Print)1613-0073

Conference

ConferenceWorkshop on Recommender Systems and the Social Web, Collocated with the 3rd ACM Conference on Recommender Systems, ACM RecSys 2009
Country/TerritoryUnited States
CityNew York, NY
Period25/10/0925/10/09

Scopus Subject Areas

  • Computer Science(all)

User-Defined Keywords

  • Collaborative filtering
  • Random walk
  • Recommender systems
  • Social relationship

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