elliot.recommender.latent_factor_models.LogisticMF package

Submodules

elliot.recommender.latent_factor_models.LogisticMF.logistic_matrix_factorization module

Module description:

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

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

get_recommendations(k: int = 100)

property name

predict(u: int, i: int)

train()

elliot.recommender.latent_factor_models.LogisticMF.logistic_matrix_factorization_model module

Module description:

class elliot.recommender.latent_factor_models.LogisticMF.logistic_matrix_factorization_model.LogisticMatrixFactorizationModel(*args, **kwargs)

Bases: tensorflow.python.keras.engine.training.Model

call(inputs, training=None, mask=None)

get_top_k(preds, train_mask, k=100)

predict_batch(start, stop, **kwargs)

set_update_user(update_user)

train_step(batch)

Module contents