Visual Models¶

Summary¶

`ACF.ACF.ACF`(data, config, params, *args, …)	Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention
`DeepStyle.DeepStyle.DeepStyle`(data, config, …)	DeepStyle: Learning User Preferences for Visual Recommendation
`DVBPR.DVBPR.DVBPR`(data, config, params, …)	Visually-Aware Fashion Recommendation and Design with Generative Image Models
`VBPR.VBPR.VBPR`(data, config, params, *args, …)	VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
`VNPR.VNPR.VNPR`(data, config, params, *args, …)	Visual Neural Personalized Ranking for Image Recommendation
`elliot.recommender.adversarial.AMR.AMR.AMR`(…)	Adversarial Multimedia Recommender

ACF¶

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

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

Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

For further details, please refer to the paper

Parameters

lr – Learning rate
epochs – Number of epochs
factors – Number of latent factors
batch_size – Batch size
l_w – Regularization coefficient
layers_component – Tuple with number of units for each attentive layer (component-level)
layers_item – Tuple with number of units for each attentive layer (item-level)

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

models:
  ACF:
    meta:
      save_recs: True
    lr: 0.0005
    epochs: 50
    factors: 100
    batch_size: 128
    l_w: 0.000025
    layers_component: (64, 1)
    layers_item: (64, 1)

DeepStyle¶

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

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

DeepStyle: Learning User Preferences for Visual Recommendation

For further details, please refer to the paper

Parameters

lr – Learning rate
epochs – Number of epochs
factors – Number of latent factors
batch_size – Batch size
batch_eval – Batch size for evaluation
l_w – Regularization coefficient

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

models:
  DeepStyle:
    meta:
      save_recs: True
    lr: 0.0005
    epochs: 50
    factors: 100
    batch_size: 128
    batch_eval: 512
    l_w: 0.000025

DVBPR¶

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

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

Visually-Aware Fashion Recommendation and Design with Generative Image Models

For further details, please refer to the paper

Parameters

lr – Learning rate
epochs – Number of epochs
factors – Number of latent factors
batch_size – Batch size
batch_eval – Batch for evaluation
lambda_1 – Regularization coefficient
lambda_2 – CNN regularization coefficient

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

models:
  DVBPR:
    meta:
      save_recs: True
    lr: 0.0001
    epochs: 50
    factors: 100
    batch_size: 128
    batch_eval: 128
    lambda_1: 0.0001
    lambda_2: 1.0

VBPR¶

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

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

VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

For further details, please refer to the paper

Parameters

lr – Learning rate
epochs – Number of epochs
factors – Number of latent factors
factors_d – Dimension of visual factors
batch_size – Batch size
batch_eval – Batch for evaluation
l_w – Regularization coefficient
l_b – Regularization coefficient of bias
l_e – Regularization coefficient of projection matrix

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

models:
  VBPR:
    meta:
      save_recs: True
    lr: 0.0005
    epochs: 50
    factors: 100
    factors_d: 20
    batch_size: 128
    batch_eval: 128
    l_w: 0.000025
    l_b: 0
    l_e: 0.002

VNPR¶

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

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

Visual Neural Personalized Ranking for Image Recommendation

For further details, please refer to the paper

Parameters

lr – Learning rate
epochs – Number of epochs
mf_factors: – Number of latent factors for Matrix Factorization:
mlp_hidden_size – Tuple with number of units for each multi-layer perceptron layer
prob_keep_dropout – Dropout rate for multi-layer perceptron
batch_size – Batch size
batch_eval – Batch for evaluation
l_w – Regularization coefficient

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

models:
  VNPR:
    meta:
      save_recs: True
    lr: 0.001
    epochs: 50
    mf_factors: 10
    mlp_hidden_size: (32, 1)
    prob_keep_dropout: 0.2
    batch_size: 64
    batch_eval: 64
    l_w: 0.001

AMR¶

class elliot.recommender.adversarial.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

The model support two adversarial perturbations methods:

FGSM-based presented by X. He et al in paper <https://arxiv.org/pdf/1809.07062.pdf>

MSAP presented by Anelli et al. in paper <https://journals.flvc.org/FLAIRS/article/view/128443>

Parameters

meta – eval_perturbations: If True Elliot evaluates the effects of both FGSM and MSAP perturbations for each validation epoch
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
eps_iter – Size of perturbations in MSAP perturbations
nb_iter – Number of Iterations in MSAP perturbations

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

models:
  AMR:
    meta:
      save_recs: True
      eval_perturbations: 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
    eps_iter: 0.00001
    nb_iter: 20
    nb_iter: 20
    eps_iter: 0.00001  # If not specified = 2.5*eps/nb_iter