The University of Southampton
University of Southampton Institutional Repository

Scalable RFM-Enriched representation learning for churn prediction

Scalable RFM-Enriched representation learning for churn prediction
Scalable RFM-Enriched representation learning for churn prediction

Most of the recent studies on churn prediction in telco utilize social networks built on top of the call (and/or SMS) graphs to derive informative features. However, extracting features from large graphs, especially structural features, is an intricate process both from a methodological and computational perspective. Due to the former, feature extraction in the current literature has mainly been addressed in an ad-hoc and handcrafted manner. Due to the latter, the full potential of the structural information is unexploited. In this work, we incorporate both interaction and structural information by devising two different ways of enriching original graphs with interaction information, delineated by the well-known RFM model. We circumvent the process of extensive manual feature engineering by enriching the networks and improving the scalability of the renowned node2vec approach to learn node representations. The obtained results demonstrate that our enriched network outperforms baseline RFM-based methods.

Churn prediction, Enriched (Social) networks, Node representation learning, RFM
79-88
IEEE
Mitrović, Sandra
106b73e6-56b8-46a4-a0ab-e9f4e3351065
Singh, Gaurav
bbbde6fe-ca69-452c-92c3-93b361792fd0
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Lemahieu, Wilfried
be4bae3f-12b9-417a-91a1-c3c264ffe068
De Weerdt, Jochen
1eaa177f-03d0-47e5-b8b6-4fb419d49e47
Mitrović, Sandra
106b73e6-56b8-46a4-a0ab-e9f4e3351065
Singh, Gaurav
bbbde6fe-ca69-452c-92c3-93b361792fd0
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Lemahieu, Wilfried
be4bae3f-12b9-417a-91a1-c3c264ffe068
De Weerdt, Jochen
1eaa177f-03d0-47e5-b8b6-4fb419d49e47

Mitrović, Sandra, Singh, Gaurav, Baesens, Bart, Lemahieu, Wilfried and De Weerdt, Jochen (2018) Scalable RFM-Enriched representation learning for churn prediction. In Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017. vol. 2018-January, IEEE. pp. 79-88 . (doi:10.1109/DSAA.2017.42).

Record type: Conference or Workshop Item (Paper)

Abstract

Most of the recent studies on churn prediction in telco utilize social networks built on top of the call (and/or SMS) graphs to derive informative features. However, extracting features from large graphs, especially structural features, is an intricate process both from a methodological and computational perspective. Due to the former, feature extraction in the current literature has mainly been addressed in an ad-hoc and handcrafted manner. Due to the latter, the full potential of the structural information is unexploited. In this work, we incorporate both interaction and structural information by devising two different ways of enriching original graphs with interaction information, delineated by the well-known RFM model. We circumvent the process of extensive manual feature engineering by enriching the networks and improving the scalability of the renowned node2vec approach to learn node representations. The obtained results demonstrate that our enriched network outperforms baseline RFM-based methods.

This record has no associated files available for download.

More information

Published date: 16 January 2018
Venue - Dates: 4th International Conference on Data Science and Advanced Analytics, DSAA 2017, , Tokyo, Japan, 2017-10-19 - 2017-10-21
Keywords: Churn prediction, Enriched (Social) networks, Node representation learning, RFM

Identifiers

Local EPrints ID: 420852
URI: http://eprints.soton.ac.uk/id/eprint/420852
PURE UUID: 2c2d11ce-4d55-4d56-a7d9-b6857c0f7390
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 17 May 2018 16:30
Last modified: 16 Mar 2024 03:39

Export record

Altmetrics

Contributors

Author: Sandra Mitrović
Author: Gaurav Singh
Author: Bart Baesens ORCID iD
Author: Wilfried Lemahieu
Author: Jochen De Weerdt

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×