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tcc2vec: RFM-informed representation learning on call graphs for churn prediction

tcc2vec: RFM-informed representation learning on call graphs for churn prediction
tcc2vec: RFM-informed representation learning on call graphs for churn prediction
Applying social network analytics for telco churn prediction has become indispensable for almost a decade. However, in the current literature, the uptake does not reflect in a significantly increased leverage of the available information that these networks convey. First, network featurization in general is a very cumbersome process due to the complex nature of networks and the lack of a respective methodology.
This results in ad hoc approaches and hand-crafted features. Second, deriving
certain structural features in very large graphs is computationally expensive and,
as a consequence, often neglected. Third, call networks are mostly treated as static in spite of their inherently dynamic nature. In this study, we propose tcc2vec, a panoptic approach aiming at devising representation learning (to address the first problem) on enriched call networks that integrate interaction and structural information (to overcome the second problem), which are being sliced in different time periods in order to account for different temporal granularities (hence addressing the third problem). In an extensive experimental analysis, insights are provided regarding an optimal choice of interaction and temporal granularities, as well as representation learning parameters.
0165-5515
Mitrović, Sandra
34d8482d-dea9-4541-9488-bce43343348f
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Lemahieu, Wilfried
b8409ed0-6c30-48c3-9fc3-92ef1f46f0e6
De Weerdta, Jochen
5666036f-0896-4cf7-bba7-b0ef31fb5efb
Mitrović, Sandra
34d8482d-dea9-4541-9488-bce43343348f
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Lemahieu, Wilfried
b8409ed0-6c30-48c3-9fc3-92ef1f46f0e6
De Weerdta, Jochen
5666036f-0896-4cf7-bba7-b0ef31fb5efb

Mitrović, Sandra, Baesens, Bart, Lemahieu, Wilfried and De Weerdta, Jochen (2019) tcc2vec: RFM-informed representation learning on call graphs for churn prediction. Journal of Information Science. (doi:10.1016/j.ins.2019.02.044).

Record type: Article

Abstract

Applying social network analytics for telco churn prediction has become indispensable for almost a decade. However, in the current literature, the uptake does not reflect in a significantly increased leverage of the available information that these networks convey. First, network featurization in general is a very cumbersome process due to the complex nature of networks and the lack of a respective methodology.
This results in ad hoc approaches and hand-crafted features. Second, deriving
certain structural features in very large graphs is computationally expensive and,
as a consequence, often neglected. Third, call networks are mostly treated as static in spite of their inherently dynamic nature. In this study, we propose tcc2vec, a panoptic approach aiming at devising representation learning (to address the first problem) on enriched call networks that integrate interaction and structural information (to overcome the second problem), which are being sliced in different time periods in order to account for different temporal granularities (hence addressing the third problem). In an extensive experimental analysis, insights are provided regarding an optimal choice of interaction and temporal granularities, as well as representation learning parameters.

Text
SandraMitrovic-IS2018 - Accepted Manuscript
Restricted to Repository staff only until 20 February 2021.
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More information

Accepted/In Press date: 17 February 2019
e-pub ahead of print date: 20 February 2019

Identifiers

Local EPrints ID: 428475
URI: https://eprints.soton.ac.uk/id/eprint/428475
ISSN: 0165-5515
PURE UUID: 70f55223-58ed-4ca9-ab55-449187ca278d
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 28 Feb 2019 17:30
Last modified: 13 Nov 2019 01:36

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