elliot.recommender.latent_factor_models.BPRMF_batch package

Submodules

elliot.recommender.latent_factor_models.BPRMF_batch.BPRMF_batch module

Module description:

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

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

get_recommendations(k: int = 100)

property name

train()

elliot.recommender.latent_factor_models.BPRMF_batch.BPRMF_batch_model module

Module description:

class elliot.recommender.latent_factor_models.BPRMF_batch.BPRMF_batch_model.BPRMF_batch_model(*args, **kwargs)

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

call(inputs, training=None)

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns

Python dictionary.

get_positions(predictions, train_mask, items, inner_test_user_true_mask)

get_top_k(predictions, train_mask, k=100)

predict(start, stop, **kwargs)

train_step(batch)

Module contents

Module description: