recpack.algorithms.TARSItemKNNVaz

class recpack.algorithms.TARSItemKNNVaz(K: int = 200, fit_decay: float = 1.1574074074074073e-05, predict_decay: float = 1.1574074074074073e-05)

Time aware variant of ItemKNN which uses a exponential decay function and pearson similarity.

Algorithm as described in Understanding the Temporal Dynamics of Recommendations across Different Rating Scales Paula Cristina Vaz, Ricardo Ribeiro, David Martins de Matos. Late-Breaking Results, Project Papers and Workshop Proceedings of the 21st Conference on User Modeling, Adaptation, and Personalization. Rome, Italy, June 10-14, 2013.

The algorithm uses an exponential decay function:

\[\Gamma(x) = e^{- \alpha \cdot \text{x}}\]

where \(\alpha\) is the decay scaling parameter, and x is the time between the maximal timestamp in the matrix and the timestamp of the event.

Parameters

K (int, optional) – How many neigbours to use per item, make sure to pick a value below the number of columns of the matrix to fit on. Defaults to 200
fit_decay (float, optional) – Defines the decay scaling used for decay during model fitting. Defaults to 1/(24*3600). This means for every day since an interaction, the value of it will be divided by ‘e’.
predict_decay (float, optional) – Defines the decay scaling used for decay during prediction. Defaults to 1/(24*3600). This means for every day since an interaction, the value of it will be divided by ‘e’.

Methods

`fit`(X)	Fit the model to the input interaction matrix.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predicts scores, given the interactions in X
`set_params`(**params)	Set the parameters of the estimator.

Attributes

`DECAY_FUNCTIONS`
`SUPPORTED_SIMILARITIES`
`identifier`	Name of the object.
`name`	Name of the object's class.

fit(X: Union[recpack.matrix.interaction_matrix.InteractionMatrix, scipy.sparse._csr.csr_matrix]) → recpack.algorithms.base.Algorithm

Fit the model to the input interaction matrix.

After fitting the model will be ready to use for prediction.

This function will handle some generic bookkeeping for each of the child classes,

The fit function gets timed, and this will get printed
Input data is converted to expected type using call to _transform_predict_input()
The model is trained using the _fit() method
_check_fit_complete() is called to check fitting was succesful

Parameters: X (Matrix) – The interactions to fit the model on.
Returns: self, fitted algorithm
Return type: Algorithm

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns: routing – A MetadataRequest encapsulating routing information.
Return type: MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

property identifier

Name of the object.

Name is made by combining the class name with the parameters passed at construction time.

Constructed by recreating the initialisation call. Example: Algorithm(param_1=value)

property name: Name of the object’s class.

predict(X: Union[recpack.matrix.interaction_matrix.InteractionMatrix, scipy.sparse._csr.csr_matrix]) → scipy.sparse._csr.csr_matrix

Predicts scores, given the interactions in X

Recommends items for each nonzero user in the X matrix.

This function is a wrapper around the _predict() method, and performs checks on in- and output data to guarantee proper computation.

Checks that model is fitted correctly
checks the output using _check_prediction() function

Parameters: X (Matrix) – interactions to predict from.
Returns: The recommendation scores in a sparse matrix format.
Return type: csr_matrix

set_params(**params)

Set the parameters of the estimator.

Parameters: params (dict) – Estimator parameters