  Recommendation systems are being explored by Cable TV operators to improve
user satisfaction with services, such as Live TV and Video on Demand (VOD)
services. More recently, Catch-up TV has been introduced, allowing users to
watch recent broadcast content whenever they want to. These services give users
a large set of options from which they can choose from, creating an information
overflow problem. Thus, recommendation systems arise as essential tools to
solve this problem by helping users in their selection, which increases not
only user satisfaction but also user engagement and content consumption. In
this paper we present a learning to rank approach that uses contextual
information and implicit feedback to improve recommendation systems for a Cable
TV operator that provides Live and Catch-up TV services. We compare our
approach with existing state-of-the-art algorithms and show that our approach
is superior in accuracy, while maintaining high scores of diversity and
serendipity.
