Recommender systems using MLlib
Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user-item association matrix. spark.ml currently supports model-based collaborative filtering, in which users and products are described by a small set of latent factors that can be used to predict missing entries. spark.ml uses the alternating least squares (ALS) algorithm to learn these latent factors. The implementation in spark.ml has the following parameters: numBlocks is the number of blocks the users and items will be partitioned into in order to parallelize computation (defaults to 10). rank is the number of latent factors in the model (defaults to 10). maxIter is the maximum number of iterations to run (defaults to 10). regParam specifies the regularization parameter in ALS (defaults to 1.0). implicitPrefs specifies whether to use the explicit feedback ALS variant or one adapted for implicit feedback data (defaults to false...