FM Waves

While I’m trying to juggle around with the data set offered by Netflix for the quest of improving their Cinematch algorithm I’m in my own quest for getting the theory behind the real model, the structure that resides behind those 2GB of user and movie ids, dates and so on.

Years ago I co-authored a paper about tastes and preferences, so I liked to carry on with this research, in order to give light to the matter (i.e. 40 movie features can be resumed in just one, “the rating”; people’s ratings are inconsistent; blah blah blah); by the way, it’s because, at the end of the day, ratings are just a set of preferences (ordinal, transitive, reflexive, but are they complete?). This doesn’t mean I’ll stop researching through machine learning, but that I’m opening two fronts.

For those fighting along my side, I’d recommend the following readings:

_Varian, H. (1992). Microeconomic Analysis. W. W. Norton & Company; 3rd edition.

                 Chapters: 7 (Utility Maximization), 8 (Choice), 19 (Time).

_Rabin, M. (1998). “Psychology and Economics”. Journal of Economic Literature, Vol.XXXVI, pp.11-46.

_Rieskamp et al. (2006). “Extending the Bounds of Rationality: Evidence and Theories of Preferential Choice”. Journal of Economic Literature, Vol.XLIV, pp.631-661.

It doesn’t mean these articles are going to help solve the problem, however are going to help understand why when we do this this and that, the result is such a given RSME.