Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2020

Abstract

Internet Protocol TeleVision (IPTV) provides many services such as live television streaming, time-shifted media, and Video On Demand (VOD). However, many customers do not engage properly with their subscribed packages due to a lack of knowledge and poor guidance. Many customers fail to identify the proper IPTV service package based on their needs and to utilise their current package to the maximum. In this paper, we propose a base-package recommendation model with a novel customer scoring-meter based on customers behaviour. Initially, our paper describes an algorithm to measure customers engagement score, which illustrates a novel approach to track customer engagement with the IPTV service provider. Next, the content-based recommendation system, which uses vector representation of subscribers and base packages details is described. We show the significance of our approach using local IPTV service provider data set qualitatively. The proposed approach can significantly improve user retention, long term revenue and customer satisfaction.

Keywords

Customer scoring, Feature engineering, Machine learning, Recommendation system, Clustering, Collaborative filtering, Content filtering, Customer Churn

Discipline

Numerical Analysis and Scientific Computing | Television | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

2020 Digital Image Computing: Techniques and Applications DICTA: Melbourne, November 29 - December 2: Proceedings

First Page

1

Last Page

8

ISBN

9781728191089

Identifier

10.1109/DICTA51227.2020.9363400

Publisher

IEEE

City or Country

Piscataway, NJ

Embargo Period

5-10-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/DICTA51227.2020.9363400

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