recpack.algorithms.STAN
- class recpack.algorithms.STAN(K: int = 200, interaction_decay: float = 0.0002777777777777778, session_decay: float = 0.0002777777777777778, distance_from_match_decay: float = 1)
Sequence and Time Aware Neighbourhoods algorithm.
Algorithm presented by Garg, Diksha, et al. “Sequence and time aware neighborhood for session-based recommendations: STAN.”
The algorithm is a modified version of UserKNN with several decay schemes applied.
Each of the user’s interactions are weighted by multiplying them with
\[e^{- \lambda_1 \, (t_{max} - t_i)}\]Where lambda_1 is the
interaction_decay
parameter.A second weighting scheme is applied when computing session similarities. The time of a session is the last timestamp in that session.
Session similarities are weighted by multiplication with
\[e^{- \lambda_2 \, |T_{s1} - T_{s_2}|}\]Where lambda_2 is the
session_decay
parameter.A final weighting is applied to recommend items closest to the last matching item between similar users. For each item in a neighbours history, the session similarity is weighted additionally by
\[e^{- \lambda_3 \, |pos_i - pos_{matching}|}\]Where lambda_3 is the
distance_from_match_decay
parameter.Note
We modified the decay computations from the paper, by using a multiplicative weight, rather than a division. This allows us to easily disable a weight. Typical values for decay parameters will be between 0 and 1.
- Parameters
K (int, optional) – The amount of sessions to be considered as neighbourhood, defaults to 200
interaction_decay (float, optional) – The decay factor for session history weighting, defaults to 1/3600
session_decay (float, optional) – The decay factor for session similarity computation, defaults to 1/3600
distance_from_match_decay (float, optional) – The decay factor for prediction item weighting, defaults to 1
Methods
fit
(X)Fit the model to the input interaction matrix.
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
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_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