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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 c
by oofbey 5y 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 5y agoI 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 5y agoI 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 5y agoIn 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.