Distributed Processing: Prediction of Ratings¶

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:

Prediction Parameters for Implicit Alternating Least Squares Computaion (Distributed Processing)¶
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 \(\mathrm{nblocks} = 3\):

Step 1 - on Local Nodes¶

Prediction of rating uses partial models, which contain the parts of user factors \(X_1, X_2, \ldots, X_{\mathrm{nblocks}}\) and item factors \(Y_1, Y_2, \ldots, Y_{\mathrm{nblocks}}\) produced at the training stage. Each pair of partial models \((X_i , Y_j)\) is used to compute a numeric table with ratings \(R_{ij}\) that correspond to the user factors and item factors from the input partial models.

Prediction with Implicit Alternating Least Squares: Distributed Processing, Step 1 - on Local Nodes¶

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 for Implicit Alternating Least Squares Computaion (Distributed Processing, Step 1)¶
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.

Output for Implicit Alternating Least Squares Computaion (Distributed Processing, Step 1)¶
Result ID	Result
`prediction`	Pointer to the \(m_i \times n_j\) numeric table with predicted ratings. Note 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`.

oneDAL documentation

Distributed Processing: Prediction of Ratings¶

Algorithm Parameters¶

Step 1 - on Local Nodes¶