How to augment matrix factors in Spark ALS recommender?

Question

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 succesfully 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 how do I interface this with a web service. Thanking you

Yes I would also know how to do this, specifically in python, did you have any luck? — Col, Jun 11 '15 at 02:59
If you're looking for model persistene see here: https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html — mtoto, Oct 22 '16 at 15:00
I've flagged this post as a duplicate - hopefully this will get it more exposure and therefore an answer. — Chris Snow, Jan 08 '17 at 16:58
See also: http://stackoverflow.com/questions/41537470/als-model-how-to-generate-full-u-vt-v — Chris Snow, Jan 08 '17 at 20:24

score 1 · Answer 1 · answered Oct 20 '17 at 08:14

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

How to augment matrix factors in Spark ALS recommender?

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