• DocumentCode
    2111173
  • Title

    Social Recommendation Based on Multi-relational Analysis

  • Author

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

  • Author_Institution
    Sch. of Software Eng., South China Univ. of Technol., Guangzhou, China
  • Volume
    2
  • fYear
    2012
  • fDate
    4-7 Dec. 2012
  • Firstpage
    471
  • Lastpage
    477
  • 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.
  • Keywords
    information retrieval; recommender systems; regression analysis; social networking (online); Epinions dataset; cold-start user problem; data sparsity; multirelational analysis; multirelational social network; optimal linear regression analysis; optimal network combination; recommendation algorithm; relation network; social recommendation method; multi-relation social network; regression analysis; social recommendation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Web Intelligence and Intelligent Agent Technology (WI-IAT), 2012 IEEE/WIC/ACM International Conferences on
  • Conference_Location
    Macau
  • Print_ISBN
    978-1-4673-6057-9
  • Type

    conf

  • DOI
    10.1109/WI-IAT.2012.222
  • Filename
    6511610