Amazon recommendations

[cellio]

I assume that Amazon's recommendations are computed largely from overlapping purchases (the "people who bought X also bought Y" approach), though they are also influenced by personal data like what I've put on my wish list and, apparently, ratings. These methods are good at capturing positive data, but they're weak for negative data. And the similarity groupings seem to be broad (not deep), which sometimes means wacky results.

There seems to be no way to tell Amazon's engine that yes, I am interested in (Jewish) biblical texts and Hebrew, but this does *not* mean I'm interested in Greek or Christian texts. There seems to be no way to tell it that I'm (currently) uninterested in books about C++, though Java and general programming are interesting. There seems to be no way to stop getting it to offer me baroque, classical, and modern music (or books on music) just because I'm interested in medieval and renaissance music. I have no idea if the copious "not interested" indications actually feed into the data pool, or if they merely act as a filter on what to show on the list.

As long as I'm going on about the recommendations scheme: there are different flavors of "not interested", and there seems to be no way to capture that. I might be uninterested because of the subject -- e.g. don't offer me books about football. I might be uninterested because I own something else that fills the same niche -- a different, similar text on Hebrew, or a compilation album that contains most of the good stuff from the CD offered. In the latter case, I don't want it to draw broad conclusions about the genre/topic (she's not interested in Hebrew any more, or she no longer likes Eric Bogle, etc). If I check "not interested" in those cases, am I sabotaging myself down the road?

I do, by the way, sometimes send questions/suggestions like these to Amazon via their feedback form, but there is absolutely no way for me to tell if that's effective (or welcome).

Comments

[sui66iy.livejournal.com]

Back in the day (circa dot.com boom) there was a brief fad for collaborative filtering technology. The idea was that we could automatically compute good recommendations using stuff like latent semantic analysis. I remember asking someone, "if the latent semantic analysis stuff is so hot, how come all the recommendation engine stuff on Amazon sucks so much?" They told me that, in practice, the good algorithms for doing this are too expensive to run, so in reality people implement pretty simple genre-based recommendations with pretty heavy input from the marketing guys about what they want to sell you today.

I have no idea if that claim is true (nor can I remember who told me). I think it's just as likely that automatic recommendation tech isn't all that great. But it's an interesting alternative possibility. I should ask Dom... he probably has some idea.

[gregbo.livejournal.com]

They told me that, in practice, the good algorithms for doing this are too expensive to run, so in reality people implement pretty simple genre-based recommendations with pretty heavy input from the marketing guys about what they want to sell you today.

I don't know for sure, but my guess is that you're correct. In any event, I'd rather have cheap(er) books and free shipping in exchange for a few negative hits on my preferences.