Production Recommendation Systems with Cloudera - Cloudera Engineering Blog

Training and selecting the best model should be automated and run at some frequency that meets the needs of your application, but is often on the order of hours. It may be run using a job scheduling service, or even as a streaming job with a long update interval. In the case of Oryx, the offline training is implemented as an operation on a Spark DStream of new user/item interaction data. The new data that has arrived within the latest batch interval is combined with historical data from the Hadoop filesystem (HDFS) and used as the training data for the Spark ML alternating least squares (ALS) algorithm.