Two-Tower Networks and Negative Sampling in Recommender Systems

Understand the key elements that power advanced recommendation engines

One of the most important types of models in recommendation systems at present is the two-tower neural networks. They are structured as follows: one part of the neural network (tower) processes all the information about the query (user, context), while the other tower processes information about the object. The outputs of these towers are embeddings, which are then multiplied (dot product or cosine, as we already discussed here). One of …

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