How to augment matrix factors in Spark ALS recommender? [duplicate]

Question

This question already has an answer here:

How to update Spark MatrixFactorizationModel for ALS 2 answers

ALS model - how to generate full_u * v^t * v? 2 answers

I am a beginner to the world of Machine Learning and the usage of Apache Spark. 
I have followed the tutorial at https://databricks-training.s3.amazonaws.com/movie-recommendation-with-mllib.html#augmenting-matrix-factors, and was successfully able to develop the application. Now, as it is required that today's web application need to be powered by real-time recommendations, I would like my model to be ready for new data that keeps coming on the server. The site has quoted:

A better way to get the recommendations for you is training a matrix factorization model first and then augmenting the model using your ratings.

How do I do that? I am using Python to develop my application. Also, please tell me how do I persist the model to use it again or an idea of how do I interface this with a web service. Thanking you

Answer 1

I don't think online learning is possible for ALS in Spark. That means you can't update the model while getting the data in real-time. However, you can use the model to get the predictions.

Also, refer to How to update Spark MatrixFactorizationModel for ALS