Generative Adversarial Networks (GANs)¶

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¶

`IRGAN.irgan.IRGAN`(data, config, params, …)	IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
`CFGAN.cfgan.CFGAN`(data, config, params, …)	CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks

IRGAN¶

class elliot.recommender.gan.IRGAN.irgan.IRGAN(data, config, params, *args, **kwargs)[source]¶

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

IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

For further details, please refer to the paper

Parameters

factors – Number of latent factor
lr – Learning rate
l_w – Regularization coefficient
l_b – Regularization coefficient of bias
l_gan – Adversarial regularization coefficient
predict_model – Specification of the model to generate the recommendation (Generator/ Discriminator)
g_epochs – Number of epochs to train the generator for each IRGAN step
d_epochs – Number of epochs to train the discriminator for each IRGAN step
g_pretrain_epochs – Number of epochs to pre-train the generator
d_pretrain_epochs – Number of epochs to pre-train the discriminator
sample_lambda – Temperature Parameters

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

models:
  IRGAN:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    l_w: 0.1
    l_b: 0.001
    l_gan: 0.001
    predict_model: generator
    g_epochs: 5
    d_epochs: 1
    g_pretrain_epochs: 10
    d_pretrain_epochs: 10
    sample_lambda: 0.2

CFGAN¶

class elliot.recommender.gan.CFGAN.cfgan.CFGAN(data, config, params, *args, **kwargs)[source]¶

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

CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks

For further details, please refer to the paper

Parameters

factors – Number of latent factor
lr – Learning rate
l_w – Regularization coefficient
l_b – Regularization coefficient of bias
l_gan – Adversarial regularization coefficient
g_epochs – Number of epochs to train the generator for each IRGAN step
d_epochs – Number of epochs to train the discriminator for each IRGAN step
s_zr – Sampling parameter of zero-reconstruction
s_pm – Sampling parameter of partial-masking

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

models:
  CFGAN:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    l_w: 0.1
    l_b: 0.001
    l_gan: 0.001
    g_epochs: 5
    d_epochs: 1
    s_zr: 0.001
    s_pm: 0.001