recpack.algorithms.ItemSimilarityMatrixAlgorithm

class recpack.algorithms.ItemSimilarityMatrixAlgorithm

Base algorithm for algorithms that fit an item to item similarity model

Model that encodes the similarity between items is expected under the similarity_matrix_ attribute.

This matrix should have shape (|items| x |items|). This can be dense or sparse matrix depending on the algorithm used.

Predictions are made by computing the dot product of the history vector of a user and the similarity matrix.

Usually a new algorithm will have to implement just the _fit() method, to construct the self.similarity_matrix_ attribute.

Methods

_check_fit_complete()	Helper function to check if model was correctly fitted
_predict(X)	Predict scores for nonzero users in X

Attributes

identifier	Name of the object.
name	Name of the object's class.

_check_fit_complete()

Helper function to check if model was correctly fitted

Checks implemented:

• Checks if the algorithm has been fitted, using sklearn’s check_is_fitted

• Checks if the fitted similarity matrix contains similar items for each item

For failing checks a warning is printed.

_predict(X: scipy.sparse._csr.csr_matrix) → scipy.sparse._csr.csr_matrix

Predict scores for nonzero users in X

Scores are computed by matrix multiplication of X with the stored similarity matrix.

Parameters

X (csr_matrix) – csr_matrix with interactions

Returns

csr_matrix with scores

Return type

csr_matrix