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.

Accessing API References

Intel® DAAL provides application programming interfaces for C++, Java*, and Python* languages. Visit Intel® Data Analytics Acceleration Library API Reference to download API References for C++, Java*, and Python*. API Reference for C++ is also available online on IDZ, see C++ API Reference for Intel® Data Analytics Acceleration Library.

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