# recpack.algorithms.TARSItemKNNLiu

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

Time aware variant of ItemKNN which uses an exponential decay function and cosine similarity.

Algorithm as described in Nathan N. Liu, Min Zhao, Evan Xiang, and Qiang Yang. 2010. Online evolutionary collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems (RecSys ‘10). Association for Computing Machinery, New York, NY, USA, 95–102. https://doi.org/10.1145/1864708.1864729

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.

Similarity is computed on this weighted matrix, using cosine similarity. At prediction time a user’s history is weighted using the same formula with a different alpha. This weighted history is then multiplied with the precomputed similarity matrix.

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 the model to the input interaction matrix. Get metadata routing of this object. get_params([deep]) Get parameters for this estimator. 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])

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 of this object.

Please check User Guide on how the routing mechanism works.

Returns

routing – A MetadataRequest encapsulating routing information.

Return type

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