How can I learn the latent vectors of a new user?

Hello. I have a question regarding your library.

Let’s suppose that I have already trained a hybrid model using a user-item rating matrix for training, and also included some user_features during training.

How can I quickly get the latent features for a new test user (for whom I also have user-features)? And how to provide top-n recommendations for that new user? Would I have to re-train the model? That would be really slow, right? How does this work exactly?

[DoronGi]

I found this helpful #210.

Basically in the hybrid model each user is represented by an identity feature, together with all other relevant features for that user. The latent representation is then just the sum of all the feature’s representations. For a new user you don’t have an identity feature, but everything else is exactly the same.

You can create a new user_features sparse matrix of size (1, num_of_features) for the new user, and use it in predict. In that case use user_id=0 when calling the function to indicate that you want to use the first (and only) row in your user_features matrix.

[DoronGi]

I believe that with this library you would have to retrain, or at least continue training (fit_partial) from your previous model (provided that you pre-prepared additional self-features for the new users).

Without additional training all you can do is to generate recommendations based on the user features, or recommend items similar to the items the user has interacted with.