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
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
numeric table with the mining data.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
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
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 PackedSymmetricMatrix , PackedTriangularMatrix , and CSRNumericTable . |