Developer Guide and Reference

  • 2021.4
  • 09/27/2021
  • Public Content
Contents

Distributed Processing: Training

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

Algorithm Parameters

At the training 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
  • step2Master
    - the second step, performed on a master node
  • step3Local
    - the third step, performed on local nodes
  • step4Local
    - the fourth step, performed on local nodes
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
or
double
.
method
fastCSR
Performance-oriented computation method for CSR numeric tables, the only method supported by the algorithm.
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.

Computation Process

At each iteration, the implicit ALS training algorithm alternates between re-computing user factors (
X
) and item factors (
Y
). These computations split each iteration into the following parts:
  1. Re-compute all user factors using the input data sets and item factors computed previously.
  2. Re-compute all item factors using input data sets in the transposed format and item factors computed previously.
Each part includes four steps executed either on local nodes or on the master node, as explained below and illustrated by graphics for LaTex Math image.. The main loop of the implicit ALS training stage is executed on the master node.

Step 1 - on Local Nodes

This step works with the matrix:
Parts of this matrix are used as input partial models.
In this step, implicit ALS recommender training 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
partialModel
Partial model computed on the local node.
In this step, implicit ALS recommender training 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
outputOfStep1ForStep2
Pointer to the LaTex Math image. numeric table with the sum of numeric tables calculated in Step 1.

Step 2 - on Master Node

This step uses local partial results from Step 1 as input.
In this step, implicit ALS recommender training 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
inputOfStep2FromStep1
A collection of numeric tables computed on local nodes in Step 1.
The collection may contain objects of any class derived from
NumericTable
except the
PackedTriangularMatrix
class with the
lowerPackedTriangularMatrix
layout.
In this step, implicit ALS recommender training 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
outputOfStep2ForStep4
Pointer to the LaTex Math image. numeric table with merged cross-products.

Step 3 - on Local Nodes

On each node
i
, this step uses results of the previous steps and requires that you provide two extra matrices Offset Table i and Input of Step 3 From Init i computed at the initialization stage of the algorithm.
The only element of the Offset Table i table refers to the:
The Input Of Step 3 From Init is a key-value data collection that refers to the
outputOfInitForComputeStep3
output of the initialization stage:
In this step, implicit ALS recommender training 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
partialModel
Partial model computed on the local node.
offset
A numeric table of size LaTex Math image. that holds the global index of the starting row of the input partial model. A part of the key-value data collection
offsets
computed at the initialization stage of the algorithm.
In this step, implicit ALS recommender training 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
outputOfStep3ForStep4
A key-value data collection that contains partial models to be used in Step 4. Each element of the collection contains an object of the
PartialModel
class.

Step 4 - on Local Nodes

This step uses the results of the previous steps and parts of the following matrix in the transposed format:
The results of the step are the re-computed parts of this matrix.
In this step, implicit ALS recommender training 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
partialModels
A key-value data collection with partial models that contain user factors/item factors computed in Step 3. Each element of the collection contains an object of the
PartialModel
class.
partialData
Pointer to the CSR numeric table that holds the
i
-th part of the input data set, assuming that the data is divided by users/items.
inputOfStep4FromStep2
Pointer to the LaTex Math image. numeric table computed in Step 2.
In this step, implicit ALS recommender training 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
outputOfStep4ForStep1
Pointer to the partial implicit ALS model that corresponds to the
i
-th data block. The partial model stores user factors/item factors.
outputOfStep4ForStep3
Pointer to the partial implicit ALS model that corresponds to the
i
-th data block. The partial model stores user factors/item factors.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.