elliot.recommender.neural.DMF package

Submodules

elliot.recommender.neural.DMF.deep_matrix_factorization module

Module description:

class elliot.recommender.neural.DMF.deep_matrix_factorization.DMF(data, config, params, *args, **kwargs)

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

Deep Matrix Factorization Models for Recommender Systems.

For further details, please refer to the paper

Parameters

• lr – Learning rate

• reg – Regularization coefficient

• user_mlp – List of units for each layer

• item_mlp – List of activation functions

• similarity – Number of factors dimension

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

models:
  DMF:
    meta:
      save_recs: True
    epochs: 10
    batch_size: 512
    lr: 0.0001
    reg: 0.001
    user_mlp: (64,32)
    item_mlp: (64,32)
    similarity: cosine

get_recommendations(k: int = 100)

property name

train()

elliot.recommender.neural.DMF.deep_matrix_factorization_model module

Module description:

class elliot.recommender.neural.DMF.deep_matrix_factorization_model.DeepMatrixFactorizationModel(*args, **kwargs)

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

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

cosine(layer_0, layer_1)

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