Collaborative Filtering - RDD-based API - Spark 2.0.2 Documentation

Collaborative Filtering - RDD-based API - Spark 2.0.2 Documentation

spark.mllib 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.mllib uses the alternating least squares (ALS) algorithm to learn these latent factors. The implementation in spark.mllib has the following parameters: numBlocks is the number of blocks used to parallelize computation (set to -1 to auto-configure). rank is the number of latent factors in the model. iterations is the number of iterations of ALS to run. ALS typically converges to a reasonable solution in 20 iterations or less. lambda specifies the regularization parameter in ALS. implicitPrefs specifies whether to use the explicit feedback ALS variant or one adapted for implicit feedback data. alpha is a parameter applicable to the implicit feedback variant of ALS that governs the baseline confidence in preference observations. Explicit vs.

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