As consumers of television are presented with a plethora of available programming, improving recommender systems in this domain is becoming increasingly important. Televisions sets, though, are often shared by multiple users whose tastes may greatly vary. Recommendation systems are challenged by this setting, since viewing data is typically col- lected and modeled per device, aggregating over its users and obscuring their individual tastes.

This paper tackles the challenge of TV recommendation, specifically aim- ing to provide recommendations for the next program to watch following the currently watched program on the device. We present an empirical evaluation of several recommendation methods over large-scale, real-life TV viewership data. Our extentions of common state-of-the-art recom- mendation methods, exploiting the current watching context, demon- strate a significant improvement in recommendation quality.