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It is not solved. Let me go into a bit of details: the current state of the art is using two-tower like models that create dense embeddings both for users and i
by marcyb5st 5y ago
It is not solved. Let me go into a bit of details: the current state of the art is using two-tower like models that create dense embeddings both for users and items. These towers are, in essence, two models that need to keep into account histories, reviews, pictures, color, material, ... . So this problem is as solved as the models that can handle these problems are.
Currently, I would expect that teams are experimenting with repurposing Attention-like architectures in some way to get better embeddings, especially from sequence like features.
- mrfox321 5y agoThere are more complex models than the one you described. That query / item tower is cheap and cache-friendly. These tend to be used for high recall. You can echew those performance benefits in favor of neural networks that allow for the features of the query and item to interact. These models are goaling for high precision.
- Mehdi2277 5y agoTwo towers are not independent of neural networks. Most two tower models I encounter are neural networks for each tower with optionally a mixing towering/layer after the two. And sometimes it's not two but even more towers depending on how your features are grouped. I would consider two towers pretty much a necessity for large corpus retrieval as step 1 in any rec system with many items and many requests. Stage 2 or later models can be heavier and be whatever you like.