elliot.recommender.latent_factor_models.CML package¶
Submodules¶
elliot.recommender.latent_factor_models.CML.CML module¶
Module description:
-
class
elliot.recommender.latent_factor_models.CML.CML.
CML
(data, config, params, *args, **kwargs)[source]¶ Bases:
elliot.recommender.recommender_utils_mixin.RecMixin
,elliot.recommender.base_recommender_model.BaseRecommenderModel
Collaborative Metric Learning
For further details, please refer to the paper
- Parameters
factors – Number of latent factors
lr – Learning rate
l_w – Regularization coefficient for latent factors
l_b – Regularization coefficient for bias
margin – Safety margin size
To include the recommendation model, add it to the config file adopting the following pattern:
models: CML: meta: save_recs: True epochs: 10 batch_size: 512 factors: 10 lr: 0.001 l_w: 0.001 l_b: 0.001 margin: 0.5
-
property
name
¶
elliot.recommender.latent_factor_models.CML.CML_model module¶
Module description:
-
class
elliot.recommender.latent_factor_models.CML.CML_model.
CML_model
(*args, **kwargs)[source]¶ Bases:
tensorflow.python.keras.engine.training.Model
-
get_config
()[source]¶ Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).
- Returns
Python dictionary.
-
Module contents¶
Module description: