lenskit.flexmf.FlexMFImplicitScorer#

class lenskit.flexmf.FlexMFImplicitScorer(config=None, **kwargs)#

Bases: lenskit.flexmf._base.FlexMFScorerBase

Implicit-feedback rating prediction with FlexMF. This is capable of realizing multiple models, including:

  • BPR-MF (Bayesian personalized ranking) [RFGSchmidtThieme09] (with "pairwise" loss)

  • Logistic matrix factorization [Joh14] (with "logistic" loss)

All use configurable negative sampling, including the sampling approach from WARP.

Stability:

Experimental

Parameters:

  • config (object | None)

  • kwargs (Any)

config: FlexMFImplicitConfig#

The component configuration object. Component classes that support configuration must redefine this attribute with their specific configuration class type, which can be a Python dataclass or a Pydantic model class.

create_trainer(data, options)#

Create a model trainer to train this model.