Recommendation Systems Usage Model
A typical workflow for methods of recommendation systems includes training and prediction, as explained below.
Algorithm-Specific Parameters
The parameters used by recommender algorithms at each stage depend on a specific algorithm.
For a list of these parameters, refer to the description of an appropriate recommender algorithm.
Training Stage
Recommendation Systems Usage Model: Training Stage

At the training stage, recommender algorithms accept 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 |
---|---|
data | Pointer to the This table can be an object of any class derived from NumericTable
except PackedTriangularMatrix and PackedSymmetricMatrix . |
At the training stage, recommender algorithms calculate 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 |
---|---|
model | Model with initialized item factors. The result can only be an object of the Model class. |
Prediction Stage
Recommendation Systems Usage Model: Prediction Stage

At the prediction stage, recommender algorithms accept 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 |
---|---|
model | Model with initialized item factors. This input can only be an object of the Model class. |
At the prediction stage, recommender algorithms calculate 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 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 PackedSymmetricMatrix , PackedTriangularMatrix , and CSRNumericTable . |