[splonk]

I have as well, and your comment matches my experience more than the article does. Different teams own different systems, and there's basically no intersection between "things that require a ton of data/computation" and "things that must be computed online".

[oofbey]

Yep. The author, as a peddler of recommendations solutions, has an incentive to convince people that this problem is very complicated, and they should hire a consultant.

In practice, good old Matrix Factorization works really well. Can you beat it with a huge team and tons of GPU hours to train fancy neural nets? Probably. Can you set up a nightly MF job on a single big machine and serve results quickly? Sure can.

[jamesblonde]

I thought the two-tower embedding model was now the go-to approach for building real-time recommendation engines?

https://www.linkedin.com/pulse/personalized-recommendations-...

[random314]

In a certain sense, matrix factorization is a special case of a TTSN with RMSE loss.

If you want cross entropy loss, use word2vec.

Follow it up with a lambdarank ranker.

[nerdponx]

I think the question of "fancy technique versus simple technique" is beside the point. Assume for the sake of argument that you have a research organization and that it's worth your while to commit their time to a recommendation system.

The point here is that you don't typically need a huge amount of computation power to serve recommendations, even if the underlying model is sophisticated and required a lot of computation to train.

Likewise for data access, the online recommendation system typically does not need full access to the databases that the researchers need access to.