TV program recommendation for groups based on muldimensional TV-anytime classifications

The advent of Digital TV and Personal Digital Recorders promise to change the way people watch TV. The higher efficiency of digital coding will lead to increasing the number of contents offered to the user, demanding automatic tools for content recommendation. In the other hand, digital recorders will permit a non-linear consumption model, enabling the creation of (automatic) personalized schedules that combine the appealing contents for a specific user or group of users. This paper presents an approach to content recommendation for groups of people, based on TV-Anytime descriptions of TV contents and semantic reasoning techniques.