Prepaid or Postpaid? That Is the Question: Novel Methods of Subscription Type Prediction in Mobile Phone Services

Abstract : In this paper, we investigate the behavioural differences between mobile phone customers with prepaid and postpaid subscriptions. Our study reveals that (a) postpaid customers are more active in terms of service usage and (b) there are strong structural correlations in the mobile phone call network as connections between customers of the same subscription type are much more frequent than those between customers of different subscription types. Based on these observations, we provide methods to detect the subscription type of customers by using information about their personal call statistics, and also their egocentric networks simultaneously. The key of our first approach is to cast this classification problem as a problem of graph labelling, which can be solved by max-flow min-cut algorithms. Our experiments show that, by using both user attributes and relationships, the proposed graph labelling approach is able to achieve a classification accuracy of ∼87%, which outperforms by ∼7% supervised learning methods using only user attributes. In our second problem, we aim to infer the subscription type of customers of external operators. We propose via approximate methods to solve this problem by using node attributes, and a two-way indirect inference method based on observed homophilic structural correlations. Our results have straightforward applications in behavioural prediction and personal marketing.
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Chapitre d'ouvrage
Social Network Based Big Data Analysis and Applications, Springer, pp.165-181, 2018, Lecture Notes in Social Networks, 〈10.1007/978-3-319-78196-9_8〉
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https://hal.inria.fr/hal-01831494
Contributeur : Márton Karsai <>
Soumis le : jeudi 5 juillet 2018 - 22:10:45
Dernière modification le : mercredi 23 janvier 2019 - 19:48:12

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Yongjun Liao, Wei Du, Màrton Karsai, Carlos Sarraute, Martin Minnoni, et al.. Prepaid or Postpaid? That Is the Question: Novel Methods of Subscription Type Prediction in Mobile Phone Services. Social Network Based Big Data Analysis and Applications, Springer, pp.165-181, 2018, Lecture Notes in Social Networks, 〈10.1007/978-3-319-78196-9_8〉. 〈hal-01831494〉

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