• DocumentCode
    3717276
  • Title

    Modeling social influences from call records and mobile web browsing histories

  • Author

    Jhao-Yin Li;Mi-Yen Yeh;Ming-Syan Chen;Jihg-Hong Lin

  • Author_Institution
    Department of Electrical Engineering, National Taiwan University
  • fYear
    2015
  • Firstpage
    1357
  • Lastpage
    1361
  • Abstract
    Nowadays, companies are usually strongly interested in discovering the latent social influences among their customers since the information is highly valuable to their marketing strategies. In this paper, we study how to model the influence probabilities among the customers of a telecommunication company by analyzing their call records and mobile web browsing histories. We first construct a directed network using the phone call records. We verify whether the statistical properties of our constructed network follow the commonly known social network properties. Next, we propose several heuristics to measure the influence probabilities between users in the constructed network by analyzing both the call records and the mobile web browsing histories. Finally, we evaluate our proposed measurements by two prediction tasks, including predicting the lengths of a call and estimating the number of common website visits between two users. The results show that our proposed measurements are effective with better prediction accuracy.
  • Keywords
    "Companies","Mobile communication","History","Predictive models","Linear regression","Big data"
  • Publisher
    ieee
  • Conference_Titel
    Big Data (Big Data), 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/BigData.2015.7363895
  • Filename
    7363895