Extracting Features from Ratings: The Role of Factor Models
  Performing effective preference-based data retrieval requires detailed and
preferentially meaningful structurized information about the current user as
well as the items under consideration. A common problem is that representations
of items often only consist of mere technical attributes, which do not resemble
human perception. This is particularly true for integral items such as movies
or songs. It is often claimed that meaningful item features could be extracted
from collaborative rating data, which is becoming available through social
networking services. However, there is only anecdotal evidence supporting this
claim; but if it is true, the extracted information could very valuable for
preference-based data retrieval. In this paper, we propose a methodology to
systematically check this common claim. We performed a preliminary
investigation on a large collection of movie ratings and present initial
evidence.
