EASEModel
- class rectools.models.ease.EASEModel(regularization: float = 500.0, num_threads: int = 1, verbose: int = 0)[source]
Bases:
ModelBase
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
fit
(dataset, *args, **kwargs)Fit model.
recommend
(users, dataset, k, filter_viewed)Recommend items for users.
recommend_to_items
(target_items, dataset, k)Recommend items for target items.
Attributes
recommends_for_cold
recommends_for_warm