recpack.algorithms.TARSItemKNN
- class recpack.algorithms.TARSItemKNN(K: int = 200, fit_decay: float = 1.1574074074074073e-05, predict_decay: float = 1.1574074074074073e-05, decay_interval: int = 1, similarity: str = 'cosine', decay_function: str = 'exponential')
Framework for time aware variants of the ItemKNN algorithm.
This class was inspired by works from Liu, Nathan N., et al. (2010), Ding et al. (2005) and Lee et al. (2007).
The framework for these approaches can be summarised as:
When training the user interaction matrix is weighted to take into account temporal information.
Similarities are computed on this weighted matrix, using various similarity measures.
When predicting the interactions are similarly weighted, giving more weight to more recent interactions.
Recommendation scores are obtained by multiplying the weighted interaction matrix with the previously computed similarity matrix.
The similarity between items is based on their decayed interaction vectors:
\[\text{sim}(i,j) = s(\Gamma(A_i), \Gamma(A_j))\]Where \(s\) is a similarity function (like
cosine
), \(\Gamma\) a decay function (likeexponential_decay
) and \(A_i\) contains the distances to now from when the users interacted with item i, if they interacted with the item at all (else the value is 0).During computation, ‘now’ is considered as the maximal timestamp in the matrix + 1. As such the age is always a positive non-zero value.
- 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)`` (one day).
predict_decay (float, optional) – Defines the decay scaling used for decay during prediction. Defaults to
1 / (24 * 3600)
(one day).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).
similarity (str, Optional) – Which similarity measure to use. Defaults to
"cosine"
.["cosine", "conditional_probability", "pearson"]
are supported.decay_function – The decay function to use, defaults to
"exponential"
. Supported values are["exponential", "log", "linear", "concave", "convex", "inverse"]
Methods
fit
(X)Fit the model to the input interaction matrix.
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
Name of the object.
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
- 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