Is dot product the right way to predict?

[JoaoLages]

While training implicit sequence models, we use losses like hinge, bpr and pointwise. These losses don't maximize directly the dot product, so why do we use it while predicting?

[maciejkula]

These losses maximize the difference between the dot products of the positive and (implicit) negative items, and so using the dot product for prediction is appropriate.

[nilansaha]

@JoaoLages A bit late to the party but what we are really optimizing here are the embeddings for user or items whatever. The dot product is a mere operation to combine the two embeddings into one result. The backprop basically goes through the dot product and changes the embeddings in such a way that we get the results we want i.e. maximize the end result for positive items and minimize the result for the negative items. Correct me if I am wrong @maciejkula