A Recommendation System Based on Implicit Data for Internet Protocol Television (IPTV)

Abstract

IPTV delivers television content over Internet Protocol (IP) networks. Videos On Demand (VOD) is the most popular IPTV, allowing users to freely select from a vast pool of program genres. Therefore, it is necessary to introduce innovative features to attract new users and retain existing ones. For this purpose, IPTV systems typically use VOD recommendation engines. The primary purpose of recommendation systems is to suggest user-relevant items from various items by producing a list of recommendations for each user. In this paper, we introduce an approach to recommendation systems in IPTV. We developed this approach on implicit feedback derived from users’ interaction with movies/series sets, such as how many times they watched a movie and how long they have spent watching specific movies/series. For the previous factors, we tested a variety of recommendation algorithms, content-based, collaborative-based, and hybrid. Then applied the previously mentioned algorithms on real-life big data sets after introducing some modifications to the algorithms, then benchmarked the results on multiple performance metrics. We noticed that the applied changes achieved promising results.