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 …

This thesis presents a context aware recommender system based on implicit-only feedback
for a televisive run-time environment. The dataset used in this thesis comprehends the past
behavior of 7921 users on 119 TV channels broadcasted both over-the-air (digital terrestrial
television-DTT) and by satellite (SAT) including free and pay-tv channels. First of all we
studied users' history and modelled their behavior adding contextual information as
suggested in the literature on context-aware recommender systems. Then we developed a …