4 ms·

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

by splonk 4y ago

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 4y ago
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.
- nerdponx 4y ago
  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.
- jamesblonde 4y ago
  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-iv-two-tower-models-gaurav-chakravorty/ https://www.linkedin.com/pulse/personalized-recommendations-...
  - random314 4y ago
    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.

4 ms·

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

by splonk 4y ago

oofbey 4y ago
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.
- nerdponx 4y ago
  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.
- jamesblonde 4y ago
  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-iv-two-tower-models-gaurav-chakravorty/ https://www.linkedin.com/pulse/personalized-recommendations-...
  - random314 4y ago
    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.