elliot.recommender.latent_factor_models.PMF package

Submodules

elliot.recommender.latent_factor_models.PMF.probabilistic_matrix_factorization module

Module description:

Mnih, Andriy, and Russ R. Salakhutdinov. “Probabilistic matrix factorization.” Advances in neural information processing systems 20 (2007)

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

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

get_recommendations(k: int = 100)

property name

predict(u: int, i: int)

train()

elliot.recommender.latent_factor_models.PMF.probabilistic_matrix_factorization_model module

Module description:

Mnih, Andriy, and Russ R. Salakhutdinov. “Probabilistic matrix factorization.” Advances in neural information processing systems 20 (2007)

class elliot.recommender.latent_factor_models.PMF.probabilistic_matrix_factorization_model.ProbabilisticMatrixFactorizationModel(*args, **kwargs)

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

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

dot_prod(layer_0, layer_1)

get_recs(inputs, training=False, **kwargs)

Get full predictions on the whole users/items matrix.

Returns

The matrix of predicted values.

get_top_k(preds, train_mask, k=100)

predict(inputs, training=False, **kwargs)

Get full predictions on the whole users/items matrix.

Returns

The matrix of predicted values.

train_step(batch)

Module contents