Generative Adversarial Networks (GANs)¶

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
    batch_size: 512
    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
    batch_size: 512
    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