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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?

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A common approach in practice is to first filter the dataset with a light model to generate a candidate set, and then apply the heavy model only to those candidates. This approach is used, for example, at YouTube (Covington et al., 2016).

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