I used Spark's ALS implementation of matrix factorization (Collaborative Filtering for Implicit Feedback) to train user and item embeddings.
Since we have a lot of users in system, I had to sample some users to train model to avoid overfitting.
Now how do I construct user embeddings for out of training users. I tried constructing user embeddings by averaging item embeddings for user's items. But when I compared performance of average vector vs original user embeddings, it is not that great.
So how would I generate user embeddings using item matrix and rating matrix?