recommender systems Web 2.0

Who wants to be recommended?

There’s a lot of ongoing research on recommender systems, fostered by the Netflix Prize.

Recommender systems are basically a software implement of some sort that allows suggestions on a given domain to be offered to users. Usually they are specialised: Amazon’s recommender system recommends books,’s recommends songs, and the like.

The key to recommendation relies into different aspects. I may be suggested things similar to things I previously chose, or things my friends like. There’s a whole theory behind this so I won’t bore you. To know more, use this site as a starting point.

My problem with RS is that of this post’s title: who wants/needs recommendation? Is it always true that I like the same kind of things? Surely, I’m a good counter example to this. I love Star Trek. I have watched and would like to watch again all single episode. Nonetheless, I hate Star Wars. I find it boring. I don’t like sci-fi in general. No Terminator, no Robocop. I can’t even name other non-trek sci-fi. So my hypothetical RS should know that I don’t like every kind of sci-fi film, but only Star Trek. Maybe my friends share this view (but as far as I know, no one really does), so it could try checking my friends’ profiles first.
If you give a look at my music library (or simply explore my profile), you could define it at least eclectic. Someone would say it’s schizoid.
Moreover, sometimes I might want to do different things from those of my friends. Negative recommendation could be part of the solution, but the underlying algorithm would just be the same.

So what would represent a good recommendation to me? Well, usually what is important to me is surprise. I like many different things. The parameters that show that I like maybe are originality, quality, …, but maybe they are simply unknown. Some people suggested a “Surpise me button” to accomplish this task. But it’s not that easy. Even if I know what I don’t like.
Hence, the final questions: how can I represent tastes of a user? How can I represent his or her reactions (or feelings) towards something he or she expects or does not? How can I represent what I would like recommendation on, and what I wouldn’t?

Stay tuned on RecSys conferences to see if someone comes out with an answer; my guess is that we’ll be seeing lots and lots of new recommender systems in the next years, and each one will be confronted with these issues.