Social recommendation based on multi-relational analysis

Jian Chen, Guanliang Chen, Haolan Zhang, Jin Huang, Gansen Zhao

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

9 Citations (Scopus)

Abstract

Social recommendation methods, often taking only one kind of relationship in social network into consideration, still faces the data sparsity and cold-start user problems. This paper presents a novel recommendation method based on multi-relational analysis: first, combine different relation networks by applying optimal linear regression analysis, and then, based on the optimal network combination, put forward a recommendation algorithm combined with multi-relational social network. The experimental results on Epinions dataset indicate that, compared with existing algorithms, can effectively alleviate data sparsity as well as cold-start issues, and achieve better performance.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2012
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages471-477
Number of pages7
ISBN (Print)9780769548807
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventIEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2012 - Macau, China
Duration: 4 Dec 20127 Dec 2012

Conference

ConferenceIEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2012
Abbreviated titleIAT 2012
Country/TerritoryChina
CityMacau
Period4/12/127/12/12

Keywords

  • multi-relation social network
  • regression analysis
  • social recommendation

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