FM Waves

Once, during a seminar in Brazil I was questioned about ethics just because I said I didn’t want to (of course, anyone like myself would consider that a provocation for reaction…). Then, I said, I’m not a philosopher, but I won’t talk about it just because ethics and values are subjective so then not discussable.

Nowadays, trying to summarise my values and having read something about corporate responsibility I can say I’d have so a better anwser, based on ethinomics could be. I won’t discuss about my ethical views because they are probably so business-orientedly-distorted that everyone would consider them irrational. And I say: NO!, THEY AIN’T IT!. Why?: because of my value system, my moral code, and my view of making business. However, don’t get me wrong, I’m no angel (but no devil…).

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.

Yesterday I was talking to my friend and colleague Pau Rausell-Köster, from the Research Unit in Cultural Economics (Universitat de València), about the Netflix Prize. We were discussing about the foundations of taste and preferences, and how it was quite difficult to, by means of a devil reductionism, create a mathematical model that could predict how you’re going to rate a movie. The question was: it works!.

This conversation though led to another mathematical model it’s been used for a while by a company called Polyphonic HMI S.L. to predict if a song will be successful (aka “a HIT”). They use a methodology they have named as “Hit Song Science”, which basically uses “Spectral Decomposition” to get different musical attributes for all the songs they have analysed (3.5 million to date). They, they apply clustering techniques to the songs that have been a success (aka “a HIT”) in the last 5 years (I imagine, the time-frame is just to take out the trends and account for changes/evolution in people’s preferences). Then, they are able to predict if a new song will succeed in the market and they asign a rating (controlling type-I error).

There’s only a downsize: would the record companies invest in promoting songs with low rating?. This would affect the song to the extent of not helping it to become a hit, so, again our beloved maths would be changing the course of events and distorting the model by means of the feedback in flawed data (the reverse, type-II error, could as well happen, bad songs evaluated as possible hits being highly promoted and succeeding). Moreover, if this happens to be in a big scale, innovation in music creation is aborted…, unless… you’re brave and forget the model!.

PS For the Netflix Prize Teams: food for thought.

“Why don’t…” is the first article I co-authored with Pau Rausell back in 1999 for the FOKUS/ACEI Joint Symposium in Viena (2000). It was an original idea from Pau regarding consumers’ preferences and why they were biased. I explain, why the hell everybody says YES when s/he is asked if reads?, whether if asked about porn everyone says NO!?. This situation he said was due to the fact of reputable vs embarrassing preferences. Well, in 2004 after some years we decided to retake the theme, and this (unpublished) article was born.

This thesis has some implications for marketing purposes, since some marketers think “customers don’t actually know what they want. In fact, when asked what they want they don’t say what they really want!!!” (most recently heard by me from Malcom Gladwell on Expomanagement 2006 here in Brazil). But my feelings are “is it just they don’t want to say what they really want because of this preferences duality???”. We’ll carry on our research.