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    政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 >  Item 140.119/84114
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/84114

    Title: Inferring Potential Users in Mobile Social Networks
    Authors: Hsu, T.-H.;Chen, C.-C.;Chiang, M.-F.;Hsu, K.-W.;Peng, W.-C.
    Contributors: 資科系
    Date: 2014-10
    Issue Date: 2016-04-11 16:04:24 (UTC+8)
    Abstract: In mobile social networks, users can communicate with each other over different telecom operators. Thus, for telecom operators, how to attract new customers is a significant issue. The work of churn prediction is to determine whether a customer would leave soon. Differing from churn prediction, our work is to find those users who are likely to join target services from the competitors in the near future, where these users are called potential users. To infer potential users, we propose a framework including feature extraction, feature selection, and classifier learning to solve the problem. First, we construct a heterogeneous information network from the call detail records of users. Then, we extract the explicit features from potential users' interaction behavior in the heterogeneous information network. Moreover, because users are influenced by their community, we extract community-based implicit features of potential users. After feature extraction, we explore the Information Gain to select the effective features. We use the effective explicit and implicit features to learn potential user classifiers, and use the classifiers to determine the potential users. Finally, we conduct experiments on real datasets. The results of our experiments show that the features extracted by our proposed method can improve the accuracy of inferring potential users.
    Relation: International Conference on Data Science and Advanced Analytics (DSAA), Shanghai, China, October 30-November 1, 2014, 347-353
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.1109/DSAA.2014.7058095
    DOI: 10.1109/DSAA.2014.7058095
    Appears in Collections:[資訊科學系] 會議論文

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