recpack.algorithms.KUNN

class recpack.algorithms.KUNN(Ku: int = 100, Ki: int = 100)

Unified Nearest Neighbour algorithm combining user and item neighbourhood methods.

KUNN Algorithm as described in ‘Unifying Nearest Neighbors Collaborative Filtering’ Verstrepen et al. (10.1145/2645710.2645731)

Computes the item KNN model and stores training interactions at fitting time. Computes the user KNN model between test and training users at prediction time.

Scores are computed as a sum of item and user similarity.

user KNN are computed using

\[sim(u,v) = \sum_{i \in I} { \frac{ R_{ui} R_{vi}}{\sqrt{c(u) c(v) c(i)}}}\]

item KNN are computed as

\[sim(i,j) = \sum_{u \in U} { \frac{ R_{ui} R_{vi}}{\sqrt{c(i) c(u) c(j)}}}\]

Similarity is computed as

\[sim(u, i) = S_U(u, i) + S_I(u, i)\]

Where user similarity is computed as

\[S_U(u, i) = \sum_{v \in KNN(u)} \frac{R_{vi} * sim(u,v)}{\sqrt{c(i)}}\]

and item similarity is computed as

\[S_I(u, i) = \sum_{j \in KNN(i)} \frac{R_{uj} * sim(i, j)}{\sqrt{c(u)}}\]

Parameters

Ku (int, optional) – How many neighbours to keep in the user similarity matrix. Defaults to 100.
Ki (int, optional) – How many items to keep as neighbours in the item similarity matrix. Defaults to 100.

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