Hey Anish,
machine learning models that are updated with incoming data are commonly
known as online learning systems. Matrix factorization is one way to
implement recommender systems, but not the only one. There are papers about
how to do online matrix factorization, but you will likely have to
implement this on your own.
Have a look at:
http://en.wikipedia.org/wiki/Recommender_system
www0.cs.ucl.ac.uk/staff/l.capra/publications/seams11-vale.pdf
Regards,
Jeff
2015-02-26 19:40 GMT+01:00 anishm anish.mashan...@gmail.com:
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
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,
Anish Mashankar
A Data Science Enthusiast
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