• DocumentCode
    2546048
  • Title

    A Big Data Model Supporting Information Recommendation in Social Networks

  • Author

    Xiaoyue Han ; Lianhua Tian ; Minjoo Yoon ; Minsoo Lee

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Ewha Womans Univ., Seoul, South Korea
  • fYear
    2012
  • fDate
    1-3 Nov. 2012
  • Firstpage
    810
  • Lastpage
    813
  • Abstract
    As information systems are becoming sophisticated and mobile, cloud computing, social networking services are now very popular to people, the amount of data is rapidly increasing every year. Big data is data which should be analyzed by a company or an organization, but has not been tried to be analyzed or could not have been processed by current technology. In this paper, we introduce a big data model for recommender systems using social network data. The model incorporates factors related to social networks and can be applied to information recommendation with respect to various social behaviors that can increase the reliability of the recommended information. The big data model has the flexibility to be expanded to incorporate more sophisticated additional factors if needed. The experimental results using it in information recommendation and using map-reduce to process it show that it is a feasible model to be used for information recommendation.
  • Keywords
    cloud computing; information systems; mobile computing; recommender systems; social networking (online); big data model; cloud computing; information recommendation; information system; map-reduce; mobile computing; recommender system; social networking service; Cloud computing; Data handling; Data models; Data storage systems; Information management; Reliability; Social network services; data model; information recommendation; social networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cloud and Green Computing (CGC), 2012 Second International Conference on
  • Conference_Location
    Xiangtan
  • Print_ISBN
    978-1-4673-3027-5
  • Type

    conf

  • DOI
    10.1109/CGC.2012.125
  • Filename
    6382911