elliot.recommender.neural.NAIS package

Submodules

elliot.recommender.neural.NAIS.nais module

Module description:

class elliot.recommender.neural.NAIS.nais.NAIS(data, config, params, *args, **kwargs)

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

NAIS: Neural Attentive Item Similarity Model for Recommendation

For further details, please refer to the paper

Parameters

• factors – Number of latent factors

• algorithm – Type of user-item factor operation (‘product’, ‘concat’)

• weight_size – List of units for each layer

• lr – Learning rate

• l_w – Regularization coefficient

• l_b – Bias regularization coefficient

• alpha – Attention factor

• beta – Smoothing exponent

• neg_ratio – Ratio of negative sampled items, e.g., 0 = no items, 1 = all un-rated items

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

models:
  NAIS:
    meta:
      save_recs: True
    factors: 100
    batch_size: 512
    algorithm: concat
    weight_size: 32
    lr: 0.001
    l_w: 0.001
    l_b: 0.001
    alpha: 0.5
    beta: 0.5
    neg_ratio: 0.5

get_recommendations(k: int = 100)

property name

train()

elliot.recommender.neural.NAIS.nais_model module

Module description:

class elliot.recommender.neural.NAIS.nais_model.LatentFactor(*args, **kwargs)

Bases: tensorflow.python.keras.layers.embeddings.Embedding

censor(censor_id)

class elliot.recommender.neural.NAIS.nais_model.NAIS_model(*args, **kwargs)

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

attention(user_history, target)

batch_attention(user_history, target)

batch_predict(user_start, user_stop)

batch_softmax(logits, item_num, similarity, user_bias, item_bias)

call(inputs, training=None)

create_history_item_matrix()

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(user, **kwargs)

softmax(logits, item_num, similarity, user_bias, item_bias, batch_mask_mat=None)

train_step(batch)

Module contents