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題名 Exploring Communication Behaviors of Users to Target Potential Users in Mobile Social Networks 作者 徐國偉
Chen, Chien-Cheng
Hsu, Kuo-Wei
Peng, Wen-Chih貢獻者 資訊科學系 關鍵詞 Communication behaviors; feature engineering; mobile social network 日期 2017-09 上傳時間 29-Aug-2017 13:24:40 (UTC+8) 摘要 In mobile communication services, users can communicate with each other over different telecommunication carriers. For telecom operators, how to acquire and retain users is a significant and practical task. Note that telecom operators only have their own customer profiles. For the users from other telecom operators, their information is sparse. Thus, given a set of communication logs, the main theme of our work is to identify the potential users who will possibly join the target services in the near future. Since only a limited amount of information is available, one challenging issue is how to extract features from the communication logs. In this article, we propose a Communication-Based Feature Generation (CBFG) framework that extracts features and builds models to infer the potential users. Explicitly, we construct a heterogeneous information network from the communication logs of users. Then, we extract the explicit features, which refer to those calling features of users, from the potential users’ interaction behaviors in the heterogeneous information network. Moreover, from the calling behaviors of users, one could extract the possible community structures of users. Based on the community structures, we further extract the implicit features of users. In light of both explicit and implicit features, we propose an information-gain-based method to select the effective features. According to the features selected, we utilize three popular classifiers (i.e., AdaBoost, Random Forest, and SVM) to build models to target the potential users. In addition, we have designed a sampling approach to extract training data for classifiers. To evaluate our methods, we have conducted experiments on a real dataset. The results of our experiments show that the features extracted by our proposed method can be effective for targeting the potential users. 關聯 ACM Transactions on Intelligent Systems and Technology (TIST) , Volume 8 Issue 6, Article No. 79 資料類型 article DOI http://dx.doi.org/10.1145/3022472 dc.contributor 資訊科學系 zh_TW dc.creator (作者) 徐國偉 zh_TW dc.creator (作者) Chen, Chien-Cheng en_US dc.creator (作者) Hsu, Kuo-Wei en_US dc.creator (作者) Peng, Wen-Chih en_US dc.date (日期) 2017-09 en_US dc.date.accessioned 29-Aug-2017 13:24:40 (UTC+8) - dc.date.available 29-Aug-2017 13:24:40 (UTC+8) - dc.date.issued (上傳時間) 29-Aug-2017 13:24:40 (UTC+8) - dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/112297 - dc.description.abstract (摘要) In mobile communication services, users can communicate with each other over different telecommunication carriers. For telecom operators, how to acquire and retain users is a significant and practical task. Note that telecom operators only have their own customer profiles. For the users from other telecom operators, their information is sparse. Thus, given a set of communication logs, the main theme of our work is to identify the potential users who will possibly join the target services in the near future. Since only a limited amount of information is available, one challenging issue is how to extract features from the communication logs. In this article, we propose a Communication-Based Feature Generation (CBFG) framework that extracts features and builds models to infer the potential users. Explicitly, we construct a heterogeneous information network from the communication logs of users. Then, we extract the explicit features, which refer to those calling features of users, from the potential users’ interaction behaviors in the heterogeneous information network. Moreover, from the calling behaviors of users, one could extract the possible community structures of users. Based on the community structures, we further extract the implicit features of users. In light of both explicit and implicit features, we propose an information-gain-based method to select the effective features. According to the features selected, we utilize three popular classifiers (i.e., AdaBoost, Random Forest, and SVM) to build models to target the potential users. In addition, we have designed a sampling approach to extract training data for classifiers. To evaluate our methods, we have conducted experiments on a real dataset. The results of our experiments show that the features extracted by our proposed method can be effective for targeting the potential users. en_US dc.format.extent 1660021 bytes - dc.format.mimetype application/pdf - dc.relation (關聯) ACM Transactions on Intelligent Systems and Technology (TIST) , Volume 8 Issue 6, Article No. 79 en_US dc.subject (關鍵詞) Communication behaviors; feature engineering; mobile social network en_US dc.title (題名) Exploring Communication Behaviors of Users to Target Potential Users in Mobile Social Networks en_US dc.type (資料類型) article - dc.identifier.doi (DOI) 10.1145/3022472 - dc.doi.uri (DOI) http://dx.doi.org/10.1145/3022472 -
