Diogo Gonçalves

Diogo Goncalves, Miguel Costa, Francisco M. Couto

Nowadays, Cable TV operators provide their users multiple ways to watch TV content, such as Live TV and Video on Demand (VOD) services. In the last years, Catch-up TV has been introduced, allowing users to watch recent broadcast content whenever they want to. Understanding how the users interact with such services is important to develop solutions that may increase user satisfaction , user engagement and user consumption. In this paper, we characterize, for the first time, how users interact with a large European Cable TV operator that provides Live TV, Catch-up TV and VOD services. We analyzed many characteristics, such as the service usage, user engagement, program type, program genres and time periods. This characterization will help us to have a deeper understanding on how users interact with these different services, that may be used to enhance the recommendation systems of Cable TV providers.

Diogo Goncalves, Miguel Costa, Francisco M. Couto

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.