• DocumentCode
    62865
  • Title

    Dynamic Latent Expertise Mining in Social Networks

  • Author

    Ofek, N. ; Shabtai, Asaf

  • Author_Institution
    Dept. of Inf. Syst. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel
  • Volume
    18
  • Issue
    5
  • fYear
    2014
  • fDate
    Sept.-Oct. 2014
  • Firstpage
    20
  • Lastpage
    27
  • Abstract
    With more individuals using social networks as well as a wider range of activities available by these platforms, there is a growing need to develop knowledge-extraction methods. This article presents ExaMine, a system for identifying expertise within a user´s social network connections. During the learning phase, the system mines the activities associated with each connection to generate profiles. When the user browses the Web, the system retrieves an ordered list of connections for any viewed webpage. It then uses a classification process to identify these connections as experts on the webpage´s dynamic topics of the webpage according to a classification process.
  • Keywords
    classification; data mining; information retrieval; learning (artificial intelligence); online front-ends; social networking (online); ExaMine system; Web browsing; Webpage dynamic topics; classification process; dynamic latent expertise mining; knowledge-extraction methods; learning phase; retrieval; social network; user connections; Data mining; Data models; Facebook; Information retrieval; Internet; Social network services; Web pages; data mining; information retrieval; social networks;
  • fLanguage
    English
  • Journal_Title
    Internet Computing, IEEE
  • Publisher
    ieee
  • ISSN
    1089-7801
  • Type

    jour

  • DOI
    10.1109/MIC.2014.83
  • Filename
    6840827