Recommendation engines: search, feeds (tiktok / youtube shorts / etc), ads, netflix suggestions, doordash suggestions, etc etc. Also happens to be my specialty.
I worked with search and ads model at Google, for most things tree models were better. What evidence do you have that neural nets are better there? I worked with large parts of Google search ranking so I know what I'm talking about, some parts you want a neural net but most of the work is done by tree models and similar, they both perform better and run faster.
I'm not sure that is true. I think inference speed is often the bottleneck for the use cases stated, as is the need for frequent re-training. As a result algorithms like catboost are very popular in those domains. I think catboost was actually invented by Yandex.
PS: Its weird that you are being down-voted. I think your opinion is reasonable.
Inference speed: more sophisticated stacks use multiple stages. Early stage might be a sublinear vector search, and the heavy hitting neural nets only rerank the remainder. Bytedance has a paper on their fairly fancy sublinear approach.
Retraining - online training solves this for the most part.
Frameworks - the only battle-tested batteries-included one I've seen is Vespa. Noone else publishes any of interesting bits. KDD is the most relevant conference if you're interested in the field. IIRC Xiaohongshu has some papers that can only really be done with NNs.
