Latent Factor Models¶

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¶

`BPRMF.BPRMF.BPRMF`(data, config, params, …)	Bayesian Personalized Ranking with Matrix Factorization
`BPRMF_batch.BPRMF_batch.BPRMF_batch`(data, …)	Batch Bayesian Personalized Ranking with Matrix Factorization
`BPRSlim.bprslim.BPRSlim`(data, config, …)	BPR Sparse Linear Methods
`CML.CML.CML`(data, config, params, *args, …)	Collaborative Metric Learning
`FFM.field_aware_factorization_machine.FFM`(…)	Field-aware Factorization Machines
`FISM.FISM.FISM`(data, config, params, *args, …)	FISM: Factored Item Similarity Models
`FM.factorization_machine.FM`(data, config, …)	Factorization Machines
`FunkSVD.funk_svd.FunkSVD`(data, config, …)	For further details, please refer to the paper
`LogisticMF.logistic_matrix_factorization.LogisticMatrixFactorization`(…)	Logistic Matrix Factorization
`MF.matrix_factorization.MF`(data, config, …)	Matrix Factorization
`NonNegMF.non_negative_matrix_factorization.NonNegMF`(…)	Non-Negative Matrix Factorization
`PMF.probabilistic_matrix_factorization.PMF`(…)	Probabilistic Matrix Factorization
`PureSVD.pure_svd.PureSVD`(data, config, …)	For further details, please refer to the paper
`Slim.slim.Slim`(data, config, params, *args, …)	Sparse Linear Methods
`SVDpp.svdpp.SVDpp`(data, config, params, …)	SVD++
`WRMF.wrmf.WRMF`(data, config, params, *args, …)	Weighted XXX Matrix Factorization

BPRMF¶

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

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

Bayesian Personalized Ranking with Matrix Factorization

For further details, please refer to the paper

Parameters

factors – Number of latent factors
lr – Learning rate
bias_regularization – Regularization coefficient for the bias
user_regularization – Regularization coefficient for user latent factors
positive_item_regularization – Regularization coefficient for positive item latent factors
negative_item_regularization – Regularization coefficient for negative item latent factors
update_negative_item_factors –
update_users –
update_items –
update_bias –

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

models:
  BPRMF:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    bias_regularization: 0
    user_regularization: 0.0025
    positive_item_regularization: 0.0025
    negative_item_regularization: 0.0025
    update_negative_item_factors: True
    update_users: True
    update_items: True
    update_bias: True

BPRMF_batch¶

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

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

Batch Bayesian Personalized Ranking with Matrix Factorization

For further details, please refer to the paper

Parameters

factors – Number of latent factors
lr – Learning rate
l_w – Regularization coefficient for latent factors
l_b – Regularization coefficient for bias

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

models:
  BPRMF_batch:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    l_w: 0.1
    l_b: 0.001

BPRSlim¶

class elliot.recommender.latent_factor_models.BPRSlim.bprslim.BPRSlim(data, config, params, *args, **kwargs)[source]¶

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

BPR Sparse Linear Methods

For further details, please refer to the paper

Parameters

factors – Number of latent factors
lr – Learning rate
lj_reg – Regularization coefficient for positive items
li_reg – Regularization coefficient for negative items

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

models:
  AMF:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    lj_reg: 0.001
    li_reg: 0.1

CML¶

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

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

Collaborative Metric Learning

For further details, please refer to the paper

Parameters

factors – Number of latent factors
lr – Learning rate
l_w – Regularization coefficient for latent factors
l_b – Regularization coefficient for bias
margin – Safety margin size

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

models:
  CML:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    l_w: 0.001
    l_b: 0.001
    margin: 0.5

FFM¶

class elliot.recommender.latent_factor_models.FFM.field_aware_factorization_machine.FFM(data, config, params, *args, **kwargs)[source]¶

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

Field-aware Factorization Machines

For further details, please refer to the paper

Parameters

factors – Number of factors of feature embeddings
lr – Learning rate
reg – Regularization coefficient

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

models:
  FFM:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    reg: 0.1

FISM¶

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

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

FISM: Factored Item Similarity Models

For further details, please refer to the paper

Parameters

factors – Number of factors of feature embeddings
lr – Learning rate
l_w – Regularization coefficient for latent factors
l_b – Regularization coefficient for bias
alpha – Alpha parameter (a value between 0 and 1)
neg_ratio –

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

models:
  FISM:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    l_w: 0.001
    l_b: 0.001
    alpha: 0.5
    neg_ratio:

FM¶

class elliot.recommender.latent_factor_models.FM.factorization_machine.FM(data, config, params, *args, **kwargs)[source]¶

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

Factorization Machines

For further details, please refer to the paper

Parameters

factors – Number of factors of feature embeddings
lr – Learning rate
reg – Regularization coefficient

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

models:
  FM:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    reg: 0.1

FunkSVD¶

class elliot.recommender.latent_factor_models.FunkSVD.funk_svd.FunkSVD(data, config, params, *args, **kwargs)[source]¶

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

For further details, please refer to the paper

Parameters

factors – Number of factors of feature embeddings
lr – Learning rate
reg_w – Regularization coefficient for latent factors
reg_b – Regularization coefficient for bias

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

models:
  FunkSVD:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    reg_w: 0.1
    reg_b: 0.001

LogisticMatrixFactorization¶

class elliot.recommender.latent_factor_models.LogisticMF.logistic_matrix_factorization.LogisticMatrixFactorization(data, config, params, *args, **kwargs)[source]¶

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

Logistic Matrix Factorization

For further details, please refer to the paper

Parameters

factors – Number of factors of feature embeddings
lr – Learning rate
reg – Regularization coefficient
alpha – Parameter for confidence estimation

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

models:
  LogisticMatrixFactorization:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    reg: 0.1
    alpha: 0.5

NonNegMF¶

class elliot.recommender.latent_factor_models.NonNegMF.non_negative_matrix_factorization.NonNegMF(data, config, params, *args, **kwargs)[source]¶

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

Non-Negative Matrix Factorization

For further details, please refer to the paper

Parameters

factors – Number of latent factors
lr – Learning rate
reg – Regularization coefficient

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

models:
  NonNegMF:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    reg: 0.1

PMF¶

class elliot.recommender.latent_factor_models.PMF.probabilistic_matrix_factorization.PMF(data, config, params, *args, **kwargs)[source]¶

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

Probabilistic Matrix Factorization

For further details, please refer to the paper

Parameters

factors – Number of latent factors
lr – Learning rate
reg – Regularization coefficient
gaussian_variance – Variance of the Gaussian distribution

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

models:
  PMF:
    meta:
      save_recs: True
    epochs: 10
    factors: 50
    lr: 0.001
    reg: 0.0025
    gaussian_variance: 0.1

PureSVD¶

class elliot.recommender.latent_factor_models.PureSVD.pure_svd.PureSVD(data, config, params, *args, **kwargs)[source]¶

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

For further details, please refer to the paper

Parameters

factors – Number of latent factors
seed – Random seed

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

models:
  PureSVD:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    seed: 42

Slim¶

class elliot.recommender.latent_factor_models.Slim.slim.Slim(data, config, params, *args, **kwargs)[source]¶

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

Sparse Linear Methods

For further details, please refer to the paper

Parameters

l1_ratio –
alpha –

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

models:
  Slim:
    meta:
      save_recs: True
    epochs: 10
    l1_ratio: 0.001
    alpha: 0.001

SVDpp¶

class elliot.recommender.latent_factor_models.SVDpp.svdpp.SVDpp(data, config, params, *args, **kwargs)[source]¶

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

SVD++

For further details, please refer to the paper

Parameters

factors – Number of latent factors
lr – Learning rate
reg_w – Regularization coefficient for latent factors
reg_b – Regularization coefficient for bias

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

models:
  SVDpp:
    meta:
      save_recs: True
    epochs: 10
    factors: 50
    lr: 0.001
    reg_w: 0.1
    reg_b: 0.001

WRMF¶

class elliot.recommender.latent_factor_models.WRMF.wrmf.WRMF(data, config, params, *args, **kwargs)[source]¶

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

Weighted XXX Matrix Factorization

For further details, please refer to the paper

Parameters

factors – Number of latent factors
lr – Learning rate
alpha –
reg – Regularization coefficient

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

models:
  WRMF:
    meta:
      save_recs: True
    epochs: 10
    factors: 50
    alpha: 1
    reg: 0.1