Catch-up TV Recommendations: Show Old Favourites and Find New Ones

Web-based catch-up TV has revolutionised watching habits as it provides users the opportunity to watch programs at their preferred time and place, using a variety of devices. With the increasing offer of TV content, there is an emer-gent need for personalised recommendation solutions, which help users to select programs of interest. In this work, we study the watching patterns of users of an Australian nation-wide catch-up TV service provider and develop a suite of approaches for a catch-up recommendation scenario. We evaluate these approaches using a new large-scale dataset gathered by the Web-based catch-up portal deployed by the provider. The evaluation allows us to compare the perfor-mance of several recommenders that address the discovery of both TV programs already watched by users and new programs that users may find relevant. Categories and Subject Descriptors H.3.4 [Information Storage and Retrieval]: System