Batch Processing¶

Training¶

For a description of the input and output, refer to Recommendation Systems Usage Model.

At the training stage, the implicit ALS recommender has the following parameters:

Training Parameters for Implicit Alternating Least Squares Computation (Batch Processing)¶
Parameter	Default Value	Description
`algorithmFPType`	`float`	The floating-point type that the algorithm uses for intermediate computations. Can be `float` or `double`.
`method`	`defaultDense`	Available computation methods: `defaultDense` - performance-oriented method `fastCSR` - performance-oriented method for CSR numeric tables
`nFactors`	\(10\)	The total number of factors.
`maxIterations`	\(5\)	The number of iterations.
`alpha`	\(40\)	The rate of confidence.
`lambda`	\(0.01\)	The parameter of the regularization.
`preferenceThreshold`	\(0\)	Threshold used to define preference values. \(0\) is the only threshold supported so far.

For a description of the input and output, refer to Recommendation Systems Usage Model.

At the prediction stage, the implicit ALS recommender has the following parameters:

Prediction Parameters for Implicit Alternating Least Squares Computation (Batch Processing)¶
Parameter	Default Value	Description
`algorithmFPType`	`float`	The floating-point type that the algorithm uses for intermediate computations. Can be `float` or `double`.
`method`	`defaultDense`	Performance-oriented computation method, the only method supported by the algorithm.

Distributed Processing

Distributed Processing: Training