elliot.recommender.neural.GeneralizedMF package¶
Submodules¶
elliot.recommender.neural.GeneralizedMF.generalized_matrix_factorization module¶
Module description:
-
class
elliot.recommender.neural.GeneralizedMF.generalized_matrix_factorization.
GMF
(data, config, params, *args, **kwargs)[source]¶ Bases:
elliot.recommender.recommender_utils_mixin.RecMixin
,elliot.recommender.base_recommender_model.BaseRecommenderModel
Neural Collaborative Filtering
For further details, please refer to the paper
- Parameters
mf_factors – Number of latent factors
lr – Learning rate
is_edge_weight_train – Whether the training uses edge weighting
To include the recommendation model, add it to the config file adopting the following pattern:
models: GMF: meta: save_recs: True epochs: 10 batch_size: 512 mf_factors: 10 lr: 0.001 is_edge_weight_train: True
-
property
name
¶
elliot.recommender.neural.GeneralizedMF.generalized_matrix_factorization_model module¶
Module description:
-
class
elliot.recommender.neural.GeneralizedMF.generalized_matrix_factorization_model.
GeneralizedMatrixFactorizationModel
(*args, **kwargs)[source]¶ Bases:
tensorflow.python.keras.engine.training.Model