elliot.recommender.latent_factor_models.MF package¶
Submodules¶
elliot.recommender.latent_factor_models.MF.matrix_factorization module¶
Module description:
-
class
elliot.recommender.latent_factor_models.MF.matrix_factorization.
MF
(data, config, params, *args, **kwargs)[source]¶ Bases:
elliot.recommender.recommender_utils_mixin.RecMixin
,elliot.recommender.base_recommender_model.BaseRecommenderModel
Matrix Factorization
For further details, please refer to the paper
- Parameters
factors – Number of latent factors
lr – Learning rate
reg – Regularization coefficient
To include the recommendation model, add it to the config file adopting the following pattern:
models: MF: meta: save_recs: True epochs: 10 batch_size: 512 factors: 10 lr: 0.001 reg: 0.1
-
property
name
¶
elliot.recommender.latent_factor_models.MF.matrix_factorization_model module¶
Module description:
-
class
elliot.recommender.latent_factor_models.MF.matrix_factorization_model.
MatrixFactorizationModel
(*args, **kwargs)[source]¶ Bases:
tensorflow.python.keras.engine.training.Model
-
get_recs
(inputs, training=False, **kwargs)[source]¶ Get full predictions on the whole users/items matrix.
- Returns
The matrix of predicted values.
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