Best workflow for iterative training

Nobody has claimed this yet.

So I've been playing with LightFM for a while to give movie recommendations to users (I'm not using the MovieLens dataset).

I've also read the LightFM documentation several times, but still have some "basic" questions of how to do some stuff.

1. Right now I just add an additional number of entries to the matrix that are users/movies that still don't exist, and whenever we have a new interaction (user, movie), we just update that index. Is there any better way to add an interaction of a new user (that isn't yet in the matrix) to the model?

2. Is there a method to predict the scores of items given certain interactions?
So instead of using the method predict() with user ID, we just send a list of interactions with movies and we get the scores of the items for those interactions.

Thanks in advance.

Start with the LightFM documentation and the predict() method mentioned in the issue. Review how users, movies, interactions, and matrix growth are currently represented, then determine whether the requested workflows are supported or require API changes. Done means a documented, agreed-upon workflow or a clearly scoped implementation plan.

Written by the indexing model from the issue text.