EASEModel

class rectools.models.ease.EASEModel(regularization: float = 500.0, num_threads: int = 1, verbose: int = 0)[source]

Bases: ModelBase[EASEModelConfig]

Embarrassingly Shallow Autoencoders for Sparse Data model.

See https://arxiv.org/abs/1905.03375.

Please note that this algorithm requires a lot of RAM during fit method. Out-of-memory issues are possible for big datasets. Reasonable catalog size for local development is about 30k items. Reasonable amount of interactions is about 20m.

Parameters

  • regularization (float) – The regularization factor of the weights.

  • verbose (int, default 0) – Degree of verbose output. If 0, no output will be provided.

  • num_threads (int, default 1) – Number of threads used for recommend method.

Inherited-members

Methods

dumps()

Serialize model to bytes.

fit(dataset, *args, **kwargs)

Fit model.

fit_partial(dataset, *args, **kwargs)

Fit model.

from_config(config)

Create model from config.

get_config([mode, simple_types])

Return model config.

get_params([simple_types, sep])

Return model parameters.

load(f)

Load model from file.

loads(data)

Load model from bytes.

recommend(users, dataset, k, filter_viewed)

Recommend items for users.

recommend_to_items(target_items, dataset, k)

Recommend items for target items.

save(f)

Save model to file.

Attributes

recommends_for_cold

recommends_for_warm

config_class

alias of EASEModelConfig