Autoencoders¶

Summary¶

`dae.multi_dae.MultiDAE`(data, config, params, …)	Collaborative denoising autoencoder
`vae.multi_vae.MultiVAE`(data, config, params, …)	Variational Autoencoders for Collaborative Filtering

MultiDAE¶

class elliot.recommender.autoencoders.dae.multi_dae.MultiDAE(data, config, params, *args, **kwargs)[source]¶

Bases: elliot.recommender.recommender_utils_mixin.RecMixin, elliot.recommender.base_recommender_model.BaseRecommenderModel

Collaborative denoising autoencoder

For further details, please refer to the paper

Parameters

intermediate_dim – Number of intermediate dimension
latent_dim – Number of latent factors
reg_lambda – Regularization coefficient
lr – Learning rate
dropout_pkeep – Dropout probaility

To include the recommendation model, add it to the config file adopting the following pattern:

models:
  MultiDAE:
    meta:
      save_recs: True
    epochs: 10
    batch_size: 512
    intermediate_dim: 600
    latent_dim: 200
    reg_lambda: 0.01
    lr: 0.001
    dropout_pkeep: 1

MultiVAE¶

class elliot.recommender.autoencoders.vae.multi_vae.MultiVAE(data, config, params, *args, **kwargs)[source]¶

Bases: elliot.recommender.recommender_utils_mixin.RecMixin, elliot.recommender.base_recommender_model.BaseRecommenderModel

Variational Autoencoders for Collaborative Filtering

For further details, please refer to the paper

Parameters

intermediate_dim – Number of intermediate dimension
latent_dim – Number of latent factors
reg_lambda – Regularization coefficient
lr – Learning rate
dropout_pkeep – Dropout probaility

To include the recommendation model, add it to the config file adopting the following pattern:

models:
  MultiVAE:
    meta:
      save_recs: True
    epochs: 10
    batch_size: 512
    intermediate_dim: 600
    latent_dim: 200
    reg_lambda: 0.01
    lr: 0.001
    dropout_pkeep: 1