Developer Guide

Contents

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:
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
X
1
,
X
2
, ...,
X
nblocks
and item factors
Y
1
,
Y
2
, ...,
Y
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.
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
m
i
x
n
j
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
.

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804