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
