The distributed processing mode assumes that the data set is split in nblocks blocks across computation nodes.

Algorithm Parameters

At the prediction stage, implicit ALS recommender in the distributed processing mode has the following parameters:

Parameter

Default Value

Description

computeStep

Not applicable

The parameter required to initialize the algorithm. Can be:

  • step1Local - the first step, performed on local nodes

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.

nFactors

10

The total number of factors.

Use the one-step computation schema for implicit ALS recommender prediction in the distributed processing mode, as explained below and illustrated by the graphic for nblocks=3:

Step 1 - on Local Nodes

Prediction of rating uses partial models, which contain the parts of user factors X1, X2, ..., Xnblocks and item factors Y1, Y2, ..., Ynblocks produced at the training stage. Each pair of partial models (Xi , Yj ) is used to compute a numeric table with ratings Rij that correspond to the user factors and item factors from the input partial models.


Implicit Alternating Least Squares, Distributed Processing, Ratings Prediction Workflow

In this step, implicit ALS recommender-based prediction accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Input ID

Input

usersPartialModel

The partial model trained by the implicit ALS algorithm in the distributed processing mode. Stores user factors that correspond to the i-th data block.

itemsPartialModel

The partial model trained by the implicit ALS algorithm in the distributed processing mode. Stores item factors that correspond to the j-th data block.

In this step, implicit ALS recommender-based prediction calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Result ID

Result

prediction

Pointer to the mi x nj numeric table with predicted ratings. By default this table is an object of the HomogenNumericTable class, but you can define it as an object of any class derived from NumericTable except PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.

有关编译器优化的更完整信息,请参阅优化通知
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