So any recommender system at scale is going to need to generate something you can use to retrieve content.

Embeddings and approximate nearest neighbor look up systems are a popular way to do this at an infrastructure level with vector databases like milvus. Most papers are targeting these systems and end up including an inner product of some embeddings and thus look a lot like classic matrix factorization.

(If you're a mathematician you might say all deep learning looks like matrix factorization, just some of it additive or otherwise non inner product)

One cool paper from bytedance (tiktok owner) that didn't generate much buzz tried to use deep learning to generate discrete retrieval codes. Apparently this works well enough to be used in production at bytedance, but the reviews and rejection on this paper are a great example of how hard it is to really set up good comparisons on public datasets.

Another cool area is multi modal learning like here... this can still be an inner product embedding but with some additional structure to allow multiple interests.

I wouldn't put that much weight on papers though. There isn't a lot of theory in this field and a lot of stuff that gets published couldn't be used in production for performance reasons. And really good stuff might not be published as it provides competitive edge.

And in practice things like how you sample your data, retraining frequency, and what features you can get into the model and how quickly you update data make a much bigger difference than model architecture.