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Comment by rhizome | Hacker News Reader

How well do any of these work?

My experience is that you can't have a generic good recommender system. It all depends on your business but if you just want a mixture between using navigation data and item data lightfm works pretty well for the generic case

I don't really mean generic. Heck, I'd be curious to know which one is the best in its most ideal use. That is, have any of them ever been really good?

I'm coming at this from the perspective that recommendation engines are, as far as I can tell, practically a hoax.

They aren't a hoax, some type of recommendation engines works really well especially the collaborative filtering types.

The reallity is that once you have a subset of items that matches your behavior recsys are learning to rank problems in the broad sense, and any kind of algorithmic feed (fb, Instagram, news, Twitter) is essentially a recsys.

There is a whole conference around these subjects with very good papers and competitions, look at the different kind of approaches to the recsys18 challenge by Spotify at the ACM site

But that gets us back to the "Theory is closer to practice in theory more than practice" problem.

some type of recommendation engines works really well especially the collaborative filtering types.

Exactly what I was asking about, because I'm not so sure. Furthermore, I think it's a category error to include collaborative filtering here, which is not automated/ML/etc.

It would be great if everybody gave their opinion about the recommendation systems they've tried. I'll start:

Netflix < MovieLens < Criticker < Coollector Movie Database

These days I never know if an angle bracket is being used as a priority indicator or inequality operator.

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