• DocumentCode
    660804
  • Title

    Friendship Prediction on Social Network Users

  • Author

    Kuan-Hsi Chen ; Liang, Tsorng-Juu

  • Author_Institution
    Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan
  • fYear
    2013
  • fDate
    8-14 Sept. 2013
  • Firstpage
    379
  • Lastpage
    384
  • Abstract
    Undoubtedly friendship is one of key factors which keep social networking service users active and the whole community expanding. Hence, predicting friendships becomes an indispensable service provided by the platforms like Plurk, Twitter and Facebook. In this study, an empirical prediction resolution is presented by taking into account the interactions among Plurk users in Taiwan. Both response links and content information extracted from the interaction corpus are used as features in the implementation of the vector space machine based prediction. Experimental results show that the presented approach outperforms those bag-of-word based methods presented in previous studies.
  • Keywords
    content-based retrieval; feature extraction; social networking (online); social sciences computing; support vector machines; user interfaces; Facebook; Plurk; Plurk user interactions; SVM model; Taiwan; Twitter; bag-of-word based methods; content information extraction; empirical prediction resolution; friendship prediction; interaction corpus; link prediction; social network users; support vector machine; vector space machine based prediction; Accuracy; Equations; Feature extraction; Message systems; Predictive models; Social network services; Support vector machines; friendship; interaction; link prediction; social network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Social Computing (SocialCom), 2013 International Conference on
  • Conference_Location
    Alexandria, VA
  • Type

    conf

  • DOI
    10.1109/SocialCom.2013.59
  • Filename
    6693356