How do I recommend items to out of training users based on its recent views? 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?
Making predictions for new users is often called the cold start problem.
This problem is difficult to over come in a purely item-to-item recommendation system. The most common solution is to include more than just item interaction information. Examples include user profile information and item content information.