lenskit.algorithms.svd

Classes

BiasedSVD(features, *[, damping, bias, ...])

Biased matrix factorization for implicit feedback using SciKit-Learn's SVD solver (sklearn.decomposition.TruncatedSVD).

class lenskit.algorithms.svd.BiasedSVD(features, *, damping=5, bias=True, algorithm='randomized')

Bases: Predictor

Biased matrix factorization for implicit feedback using SciKit-Learn’s SVD solver (sklearn.decomposition.TruncatedSVD). It operates by first computing the bias, then computing the SVD of the bias residuals.

You’ll generally want one of the iterative SVD implementations such as lennskit.algorithms.als.BiasedMF; this is here primarily as an example and for cases where you want to evaluate a pure SVD implementation.

Parameters:

• bias (Bias)

• algorithm (Literal['arpack', 'randomized'])

fit(data, **kwargs)

Train a model using the specified ratings (or similar) data.

Parameters:

• data (Dataset) – The training data.

• kwargs – Additional training data the algorithm may require. Algorithms should avoid using the same keyword arguments for different purposes, so that they can be more easily hybridized.

Returns:

The algorithm object.

predict_for_user(user, items, ratings=None)

Compute predictions for a user and items.

Parameters:

• user – the user ID

• items (array-like) – the items to predict

• ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.

Returns:

scores for the items, indexed by item id.

Return type:

pandas.Series

get_params(deep=True)

Get the parameters for this algorithm (as in scikit-learn). Algorithm parameters should match constructor argument names.

The default implementation returns all attributes that match a constructor parameter name. It should be compatible with sklearn.base.BaseEstimator.get_params() method so that LensKit alogrithms can be cloned with sklearn.base.clone() as well as lenskit.util.clone().

Returns:

the algorithm parameters.

Return type:

dict