The current explosion of the number of available channels is making the choice of the program to watch an experience more and more difficult for TV viewers. Such a huge amount obliges the users to spend a lot of time in consulting TV guides and reading synopsis, with a heavy risk of even missing what really would have interested them. In this paper we confront this problem by developing a recommender system for TV programs. Recommender systems have been widely studied in the video-on-demand field, but the TV domain poses its own challenges which make the traditional video-on-demand techniques not suitable. In more detail, we propose recommendation algorithms relying exclusively on implicit feedback and leveraging context information. An extensive evaluation on a real TV dataset proves the effectiveness of our approach, and in particular the importance of the context in providing TV program recommendations