elliot.recommender.latent_factor_models.FISM package¶
Submodules¶
elliot.recommender.latent_factor_models.FISM.FISM module¶
Module description:
-
class
elliot.recommender.latent_factor_models.FISM.FISM.
FISM
(data, config, params, *args, **kwargs)[source]¶ Bases:
elliot.recommender.recommender_utils_mixin.RecMixin
,elliot.recommender.base_recommender_model.BaseRecommenderModel
FISM: Factored Item Similarity Models
For further details, please refer to the paper
- Parameters
factors – Number of factors of feature embeddings
lr – Learning rate
beta – Regularization coefficient for latent factors
lambda – Regularization coefficient for user bias
gamma – Regularization coefficient for item bias
alpha – Alpha parameter (a value between 0 and 1)
neg_ratio – ratio of sampled negative items
To include the recommendation model, add it to the config file adopting the following pattern:
models: FISM: meta: save_recs: True epochs: 10 batch_size: 512 factors: 10 lr: 0.001 alpha: 0.5 beta: 0.001 lambda: 0.001 gamma: 0.001 neg_ratio: 0.5
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property
name
¶
elliot.recommender.latent_factor_models.FISM.FISM_model module¶
Module description:
-
class
elliot.recommender.latent_factor_models.FISM.FISM_model.
FISM_model
(*args, **kwargs)[source]¶ Bases:
tensorflow.python.keras.engine.training.Model
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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.
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Module contents¶
Module description: