TY - CHAP
T1 - Prepaid or Postpaid? That Is the Question: Novel Methods of Subscription Type Prediction in Mobile Phone Services
AU - Liao, Yongjun
AU - Du, Wei
AU - Karsai, Márton
AU - Sarraute, Carlos
AU - Minnoni, Martin
AU - Fleury, Eric
N1 - Funding Agency and Grant Number: SticAmSud UCOOL project; INRIA; SoSweet [ANR-15-CE38-0011-01]; CODDDE [ANR-13-CORD-0017-01] Funding text: We acknowledge Grandata to share the data and M. Fixman for his technical support. This research project was partially granted by the SticAmSud UCOOL project, INRIA, SoSweet (ANR-15-CE38-0011-01) and CODDDE (ANR-13-CORD-0017-01).
PY - 2018
Y1 - 2018
N2 - 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 similar to 87%, which outperforms by similar to 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.
AB - 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 similar to 87%, which outperforms by similar to 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.
UR - https://m2.mtmt.hu/api/publication/32842021
U2 - 10.1007/978-3-319-78196-9_8
DO - 10.1007/978-3-319-78196-9_8
M3 - Chapter
SN - 9783319781952
T3 - Lecture Notes in Social Networks, ISSN 2190-5428
SP - 165
EP - 181
BT - Social Network Based Big Data Analysis and Applications
A2 - Day, MY
A2 - Khoury, S
A2 - Kawash, J
A2 - Kaya, M
PB - Springer-Verlag Wien
CY - Vienna
ER -