Latent Factor Models¶

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, …)	Train a Sparse Linear Methods (SLIM) item similarity model.
`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
    batch_size: 512
    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
    batch_size: 512
    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
    batch_size: 512
    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
    batch_size: 512
    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
beta – Regularization coefficient for latent factors
lambda – Regularization coefficient for user bias
gamma – Regularization coefficient for item bias
alpha – Alpha parameter (a value between 0 and 1)
neg_ratio – ratio of sampled negative items

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

models:
  FISM:
    meta:
      save_recs: True
    epochs: 10
    batch_size: 512
    factors: 10
    lr: 0.001
    alpha: 0.5
    beta: 0.001
    lambda: 0.001
    gamma: 0.001
    neg_ratio: 0.5

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
    batch_size: 512
    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
    batch_size: 512
    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
    batch_size: 512
    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
    batch_size: 512
    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
    batch_size: 512
    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
    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

Train a Sparse Linear Methods (SLIM) item similarity model.

NOTE: ElasticNet solver is parallel, a single intance of SLIM_ElasticNet will

make use of half the cores available

See:

Efficient Top-N Recommendation by Linear Regression, M. Levy and K. Jack, LSRS workshop at RecSys 2013.

SLIM: Sparse linear methods for top-n recommender systems, X. Ning and G. Karypis, ICDM 2011. 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
    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
    batch_size: 512
    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