Base-package recommendation framework based on consumer behaviours in IPTV platform

• Main Authors: SHANMUGALINGAM, Kuruparan, RANGANAYANKE, Ruwinda, GUNAWARDHAHA, Chanka, NAVARATHNA, Rajitha
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Customer scoring; Feature engineering; Machine learning; Recommendation system; Clustering; Collaborative filtering; Content filtering; Customer Churn; Numerical Analysis and Scientific Computing; Television; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5917; https://ink.library.smu.edu.sg/context/sis_research/article/6920/viewcontent/Base_Package_IPTV_2020.pdf
• Institution: Singapore Management University

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.