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.
|Journal||ACM Transactions on Intelligent Systems and Technology|
|State||Published - 1 Aug 2017|
- Communication behaviors
- Feature engineering
- Mobile social network