Usage Model: Training and Prediction

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

Recommender Training Workflow

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 `m` x `n` 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

Recommender Prediction Workflow

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 `m` x `n` 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.