Autoencoders

Elliot integrates, to date, 50 recommendation models partitioned into two sets. The first set includes 38 popular models implemented in at least two of frameworks reviewed in this work (i.e., adopting a framework-wise popularity notion).

Summary

dae.multi_dae.MultiDAE(data, config, params, …)

Variational Autoencoders for Collaborative Filtering

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

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