elliot.recommender.neural.NeuMF package¶
Submodules¶
elliot.recommender.neural.NeuMF.neural_matrix_factorization module¶
Module description:
-
class
elliot.recommender.neural.NeuMF.neural_matrix_factorization.
NeuMF
(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 MF latent factors
mlp_factors – Number of MLP latent factors
mlp_hidden_size – List of units for each layer
lr – Learning rate
dropout – Dropout rate
is_mf_train – Whether to train the MF embeddings
is_mlp_train – Whether to train the MLP layers
To include the recommendation model, add it to the config file adopting the following pattern:
models: NeuMF: meta: save_recs: True epochs: 10 batch_size: 512 mf_factors: 10 mlp_factors: 10 mlp_hidden_size: (64,32) lr: 0.001 dropout: 0.0 is_mf_train: True is_mlp_train: True
-
property
name
¶
elliot.recommender.neural.NeuMF.neural_matrix_factorization_model module¶
Module description:
-
class
elliot.recommender.neural.NeuMF.neural_matrix_factorization_model.
NeuralMatrixFactorizationModel
(*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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