I am going to implement a recommender system based on this paper. It basically uses a double embedding technique, one for the user representation and another one for the products (movies, clothes, whatever you are trying to sell.)
I understand thay you can easily train the embeddings based on a Boolean target that represents the interest of the user in a certain product. Let's say that the output layer of our NN would be a simple sigmoid (so we can assume this output as the probability that the user likes the product).
Once the training is done, I understand you want to recommend the $n$ top products. But if your dataset contains millions of products you cannot find the real top $n$, I guess some approximation or simplification should be used, but I could not find any information related to this. Any suggestion?