Artificial Neural Networks¶

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¶

`ConvMF.convolutional_matrix_factorization.ConvMF`(…)	Convolutional Matrix Factorization for Document Context-Aware Recommendation
`ConvNeuMF.convolutional_neural_matrix_factorization.ConvNeuMF`(…)	Outer Product-based Neural Collaborative Filtering
`DeepFM.deep_fm.DeepFM`(data, config, params, …)	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
`DMF.deep_matrix_factorization.DMF`(data, …)	Deep Matrix Factorization Models for Recommender Systems.
`GeneralizedMF.generalized_matrix_factorization.GMF`(…)	Neural Collaborative Filtering
`ItemAutoRec.itemautorec.ItemAutoRec`(data, …)	AutoRec: Autoencoders Meet Collaborative Filtering (Item-based)
`NAIS.nais.NAIS`(data, config, params, *args, …)	NAIS: Neural Attentive Item Similarity Model for Recommendation
`NeuMF.neural_matrix_factorization.NeuMF`(…)	Neural Collaborative Filtering
`NFM.neural_fm.NFM`(data, config, params, …)	Neural Factorization Machines for Sparse Predictive Analytics
`NPR.neural_personalized_ranking.NPR`(data, …)	Neural Personalized Ranking for Image Recommendation (Model without visual features)
`UserAutoRec.userautorec.UserAutoRec`(data, …)	AutoRec: Autoencoders Meet Collaborative Filtering (User-based)
`WideAndDeep.wide_and_deep.WideAndDeep`(data, …)	Wide & Deep Learning for Recommender Systems

ConvMF¶

class elliot.recommender.neural.ConvMF.convolutional_matrix_factorization.ConvMF(data, config, params, *args, **kwargs)[source]¶

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

Convolutional Matrix Factorization for Document Context-Aware Recommendation

For further details, please refer to the paper

Parameters

embedding_size – Embedding dimension
lr – Learning rate
l_w – Regularization coefficient
l_b – Regularization coefficient of bias
cnn_channels – List of channels
cnn_kernels – List of kernels
cnn_strides – List of strides
dropout_prob – Dropout probability applied on the convolutional layers

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

models:
  ConvMF:
    meta:
      save_recs: True
    epochs: 10
    embedding_size: 100
    lr: 0.001
    l_w: 0.005
    l_b: 0.0005
    cnn_channels: (1, 32, 32)
    cnn_kernels: (2,2)
    cnn_strides: (2,2)
    dropout_prob: 0

ConvNeuMF¶

class elliot.recommender.neural.ConvNeuMF.convolutional_neural_matrix_factorization.ConvNeuMF(data, config, params, *args, **kwargs)[source]¶

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

Outer Product-based Neural Collaborative Filtering

For further details, please refer to the paper

Parameters

embedding_size – Embedding dimension
lr – Learning rate
l_w – Regularization coefficient
l_b – Regularization coefficient of bias
cnn_channels – List of channels
cnn_kernels – List of kernels
cnn_strides – List of strides
dropout_prob – Dropout probability applied on the convolutional layers

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

models:
  ConvNeuMF:
    meta:
      save_recs: True
    epochs: 10
    embedding_size: 100
    lr: 0.001
    l_w: 0.005
    l_b: 0.0005
    cnn_channels: (1, 32, 32)
    cnn_kernels: (2,2)
    cnn_strides: (2,2)
    dropout_prob: 0

DeepFM¶

class elliot.recommender.neural.DeepFM.deep_fm.DeepFM(data, config, params, *args, **kwargs)[source]¶

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

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

For further details, please refer to the paper

Parameters

factors – Number of factors dimension
lr – Learning rate
l_w – Regularization coefficient
hidden_neurons – List of units for each layer
hidden_activations – List of activation functions

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

models:
  DeepFM:
    meta:
      save_recs: True
    epochs: 10
    factors: 100
    lr: 0.001
    l_w: 0.0001
    hidden_neurons: (64,32)
    hidden_activations: ('relu','relu')

DMF¶

class elliot.recommender.neural.DMF.deep_matrix_factorization.DMF(data, config, params, *args, **kwargs)[source]¶

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

Deep Matrix Factorization Models for Recommender Systems.

For further details, please refer to the paper

Parameters

lr – Learning rate
reg – Regularization coefficient
user_mlp – List of units for each layer
item_mlp – List of activation functions
similarity – Number of factors dimension

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

models:
  DMF:
    meta:
      save_recs: True
    epochs: 10
    lr: 0.0001
    reg: 0.001
    user_mlp: (64,32)
    item_mlp: (64,32)
    similarity: cosine

GMF¶

class elliot.recommender.neural.GeneralizedMF.generalized_matrix_factorization.GMF(data, config, params, *args, **kwargs)[source]¶

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

Neural Collaborative Filtering

For further details, please refer to the paper

Parameters

mf_factors – Number of latent factors
lr – Learning rate
is_edge_weight_train – Whether the training uses edge weighting

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

models:
  GMF:
    meta:
      save_recs: True
    epochs: 10
    mf_factors: 10
    lr: 0.001
    is_edge_weight_train: True

ItemAutoRec¶

class elliot.recommender.neural.ItemAutoRec.itemautorec.ItemAutoRec(data, config, params, *args, **kwargs)[source]¶

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

AutoRec: Autoencoders Meet Collaborative Filtering (Item-based)

For further details, please refer to the paper

Parameters

hidden_neuron – List of units for each layer
lr – Learning rate
l_w – Regularization coefficient

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

models:
  ItemAutoRec:
    meta:
      save_recs: True
    epochs: 10
    hidden_neuron: 500
    lr: 0.0001
    l_w: 0.001

NAIS¶

class elliot.recommender.neural.NAIS.nais.NAIS(data, config, params, *args, **kwargs)[source]¶

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

NAIS: Neural Attentive Item Similarity Model for Recommendation

For further details, please refer to the paper

Parameters

factors – Number of latent factors
algorithm – Type of user-item factor operation (‘product’, ‘concat’)
weight_size – List of units for each layer
lr – Learning rate
l_w – Regularization coefficient
l_b – Bias regularization coefficient
alpha – Attention factor
beta – Smoothing exponent
neg_ratio – Ratio of negative sampled items, e.g., 0 = no items, 1 = all un-rated items

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

models:
  NAIS:
    meta:
      save_recs: True
    factors: 100
    algorithm: concat
    weight_size: 32
    lr: 0.001
    l_w: 0.001
    l_b: 0.001
    alpha: 0.5
    beta: 0.5
    neg_ratio: 0.5

NeuMF¶

class elliot.recommender.neural.NeuMF.neural_matrix_factorization.NeuMF(data, config, params, *args, **kwargs)[source]¶

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

Neural Collaborative Filtering

For further details, please refer to the paper

Parameters

mf_factors – Number of MF latent factors
mlp_factors – Number of MLP latent factors
mlp_hidden_size – List of units for each layer
lr – Learning rate
dropout – Dropout rate
is_mf_train – Whether to train the MF embeddings
is_mlp_train – Whether to train the MLP layers

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

models:
  NeuMF:
    meta:
      save_recs: True
    epochs: 10
    mf_factors: 10
    mlp_factors: 10
    mlp_hidden_size: (64,32)
    lr: 0.001
    dropout: 0.0
    is_mf_train: True
    is_mlp_train: True

NFM¶

class elliot.recommender.neural.NFM.neural_fm.NFM(data, config, params, *args, **kwargs)[source]¶

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

Neural Factorization Machines for Sparse Predictive Analytics

For further details, please refer to the paper

Parameters

factors – Number of factors dimension
lr – Learning rate
l_w – Regularization coefficient
hidden_neurons – List of units for each layer
hidden_activations – List of activation functions

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

models:
  NFM:
    meta:
      save_recs: True
    epochs: 10
    factors: 100
    lr: 0.001
    l_w: 0.0001
    hidden_neurons: (64,32)
    hidden_activations: ('relu','relu')

NPR¶

class elliot.recommender.neural.NPR.neural_personalized_ranking.NPR(data, config, params, *args, **kwargs)[source]¶

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

Neural Personalized Ranking for Image Recommendation (Model without visual features)

For further details, please refer to the paper

Parameters

mf_factors – Number of MF latent factors
mlp_hidden_size – List of units for each layer
lr – Learning rate
l_w – Regularization coefficient
dropout – Dropout rate

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

models:
  NPR:
    meta:
      save_recs: True
    epochs: 10
    mf_factors: 100
    mlp_hidden_size:  (64,32)
    lr: 0.001
    l_w: 0.001
    dropout: 0.45

UserAutoRec¶

class elliot.recommender.neural.UserAutoRec.userautorec.UserAutoRec(data, config, params, *args, **kwargs)[source]¶

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

AutoRec: Autoencoders Meet Collaborative Filtering (User-based)

For further details, please refer to the paper

Parameters

hidden_neuron – List of units for each layer
lr – Learning rate
l_w – Regularization coefficient

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

models:
  UserAutoRec:
    meta:
      save_recs: True
    epochs: 10
    hidden_neuron: 500
    lr: 0.0001
    l_w: 0.001

WideAndDeep¶

class elliot.recommender.neural.WideAndDeep.wide_and_deep.WideAndDeep(data, config, params, *args, **kwargs)[source]¶

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

Wide & Deep Learning for Recommender Systems

(For now, available with knowledge-aware features)

For further details, please refer to the paper

Parameters

factors – Number of latent factors
mlp_hidden_size – List of units for each layer
lr – Learning rate
l_w – Regularization coefficient
l_b – Bias Regularization Coefficient
dropout_prob – Dropout rate

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

models:
  NPR:
    meta:
      save_recs: True
    epochs: 10
    factors: 50
    mlp_hidden_size: (32, 32, 1)
    lr: 0.001
    l_w: 0.005
    l_b: 0.0005
    dropout_prob: 0.0