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 > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Augment-more-data-to-existing-MatrixFactorization-Model-tp21830.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > >
