  Recommendation is usually reduced to a prediction problem over the function
$r(u_a, e_i)$ that returns the expected rating of element $e_i$ for user $u_a$.
In the IPTV domain, we deal with an environment where the definitions of all
the parameters involved in this function (i.e., user profiles, feedback ratings
and elements) are controversial. To our knowledge, this paper represents the
first attempt to run collaborative filtering algorithms without inner
assumptions: we start our analysis from an unstructured set of recordings,
before performing a data pre-processing phase in order to extract useful
information. Hence, we experiment with a real Digital Video Recorder system
where EPG have not been provided to the user for selecting event timings and
where explicit feedbacks were not collected.
