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 userrelevant 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.