recpack.algorithms.TARSItemKNNHermann

class recpack.algorithms.TARSItemKNNHermann(K: int = 200, decay_interval: int = 1)

Time aware variant of ItemKNN that considers the time between two interactions when computing similarity between two items, as well as the age of an event.

Presented in Hermann, C. (2010). Time-Based Recommendations for Lecture Materials. In J. Herrington & C. Montgomerie (Eds.), Proceedings of ED-MEDIA 2010–World Conference on Educational Multimedia, Hypermedia & Telecommunications (pp. 1028-1033). Toronto, Canada: Association for the Advancement of Computing in Education (AACE).

Similarity between two items is computed as the avg of \(S_{u,i,j}\) for each user that has seen both items i and j.

\[S_{u,i,j} = \frac{1}{\Delta t_{u,i,j} + \Delta d_{u,i,j}}\]

where \(\Delta t_{u,i,j}\) is the distance in time units between the user interacting with item i and j. \(\Delta d_{u,i,j}\) is the maximal distance in time units between a user interactions with i or j to now.

Parameters

K (int, optional) – Number of 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.
decay_interval (int, optional) – Size of a single time unit in seconds. Allows more finegrained parameters for large scale datasets where events are collected over months of data. Defaults to 1 (second).

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

`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