Adversarial Learning¶

Summary¶

`AMF.AMF.AMF`(data, config, params, *args, …)	Adversarial Matrix Factorization
`AMR.AMR.AMR`(data, config, params, *args, …)	Adversarial Multimedia Recommender

AMF¶

class elliot.recommender.adversarial.AMF.AMF.AMF(data, config, params, *args, **kwargs)[source]¶

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

Adversarial Matrix Factorization

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
eps – Perturbation Budget
l_adv – Adversarial regularization coefficient
adversarial_epochs – Adversarial epochs

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

models:
  AMF:
    meta:
      save_recs: True
    epochs: 10
    batch_size: 512
    factors: 200
    lr: 0.001
    l_w: 0.1
    l_b: 0.001
    eps: 0.1
    l_adv: 0.001
    adversarial_epochs: 10

AMR¶

class elliot.recommender.adversarial.AMR.AMR.AMR(data, config, params, *args, **kwargs)[source]¶

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

Adversarial Multimedia Recommender

For further details, please refer to the paper

Parameters

factors – Number of latent factor
factors_d – Image-feature dimensionality
lr – Learning rate
l_w – Regularization coefficient
l_b – Regularization coefficient of bias
l_e – Regularization coefficient of image matrix embedding
eps – Perturbation Budget
l_adv – Adversarial regularization coefficient
adversarial_epochs – Adversarial epochs

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

models:
  AMR:
    meta:
      save_recs: True
    epochs: 10
    batch_size: 512
    factors: 200
    factors_d: 20
    lr: 0.001
    l_w: 0.1
    l_b: 0.001
    l_e: 0.1
    eps: 0.1
    l_adv: 0.001
    adversarial_epochs: 5