elliot.recommender.neural.ConvNeuMF package¶
Submodules¶
elliot.recommender.neural.ConvNeuMF.convolutional_neural_matrix_factorization module¶
Module description:
-
class
elliot.recommender.neural.ConvNeuMF.convolutional_neural_matrix_factorization.
ConvNeuMF
(data, config, params, *args, **kwargs)[source]¶ Bases:
elliot.recommender.recommender_utils_mixin.RecMixin
,elliot.recommender.base_recommender_model.BaseRecommenderModel
Outer Product-based Neural Collaborative Filtering
For further details, please refer to the paper
- Parameters
embedding_size – Embedding dimension
lr – Learning rate
l_w – Regularization coefficient
l_b – Regularization coefficient of bias
cnn_channels – List of channels
cnn_kernels – List of kernels
cnn_strides – List of strides
dropout_prob – Dropout probability applied on the convolutional layers
To include the recommendation model, add it to the config file adopting the following pattern:
models: ConvNeuMF: meta: save_recs: True epochs: 10 batch_size: 512 embedding_size: 100 lr: 0.001 l_w: 0.005 l_b: 0.0005 cnn_channels: (1, 32, 32) cnn_kernels: (2,2) cnn_strides: (2,2) dropout_prob: 0
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property
name
¶
elliot.recommender.neural.ConvNeuMF.convolutional_neural_matrix_factorization_model module¶
Module description:
-
class
elliot.recommender.neural.ConvNeuMF.convolutional_neural_matrix_factorization_model.
ConvNeuralMatrixFactorizationModel
(*args, **kwargs)[source]¶ Bases:
tensorflow.python.keras.engine.training.Model
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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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