The Importance of Context When Recommending TV Content: Dataset and Algorithms

The underlying factors affecting users' choices of what to watch on TV have for several years been of interest to commercial and academic research. In the midst of a rapidly changing device and multimedia landscape, TVs continue to be at the core of multimedia consumption in the home with scenarios covering, among others, social gatherings and solitary immersive moments. The inherent complexity of viewing situations challenges the creation of experiences that match personal preferences as well as temporal and social contexts. Due to the increased availability of multimedia, research has been focused on improving the users' decision process by reducing large catalogs of content to a few personalized suggestions [1]. Commercial recommender solutions are now considered core to the business of engaging users and thereby preventing abandonment [2]. To do so, recommender systems have explored various features for personalization, such as history of watching, ratings, user/item similarity, and time of the day, the last of which is an example of features characteristic to context-aware recommender systems (CARS) [3]. The main objective of a recommender system is to personalize the experience to the individual, often by studying the user-item matrix. This could be an issue, since an account on a TV is often shared by multiple members of a household that end up diluting the user profile.