Reduce serving time complexity for real-time recommender systems I am working on a real-time recommender system predicting a product to a user using deep learning techniques (like wide &amp; deep learning, deep &amp; cross-network etc). Product catalogue can be huge (1000s to 1 million) and for a given user, the model needs to be evaluated against each product in real-time. As scalability is an important concern, is there any way to reduce the serving time complexity by tuning model architecture?
You write ... the model needs to be evaluated against each product in real-time., which gets me thinking that you use a binary classification (sigmoid in the final layer) architecture with negative sampling for the user/item interactions when training your model.
Have you considered using multi-class classification instead? Thus, for the user only predict once for the entire product catalogue, and selecting the top-k candidates from the softmax-layer. This way, you only need to feed-forward once through your neural net during inference.