deepctr.models.sequence.dien module

Author:

Weichen Shen, weichenswc@163.com

Reference:

[1] Zhou G, Mou N, Fan Y, et al. Deep Interest Evolution Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1809.03672, 2018. (https://arxiv.org/pdf/1809.03672.pdf)

deepctr.models.sequence.dien.DIEN(dnn_feature_columns, history_feature_list, gru_type='GRU', use_negsampling=False, alpha=1.0, use_bn=False, dnn_hidden_units=(256, 128, 64), dnn_activation='relu', att_hidden_units=(64, 16), att_activation='dice', att_weight_normalization=True, l2_reg_dnn=0, l2_reg_embedding=1e-06, dnn_dropout=0, seed=1024, task='binary')

Instantiates the Deep Interest Evolution Network architecture.

Parameters

• dnn_feature_columns – An iterable containing all the features used by deep part of the model.

• history_feature_list – list,to indicate sequence sparse field

• gru_type – str,can be GRU AIGRU AUGRU AGRU

• use_negsampling – bool, whether or not use negtive sampling

• alpha – float ,weight of auxiliary_loss

• use_bn – bool. Whether use BatchNormalization before activation or not in deep net

• dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of DNN

• dnn_activation – Activation function to use in DNN

• att_hidden_units – list,list of positive integer , the layer number and units in each layer of attention net

• att_activation – Activation function to use in attention net

• att_weight_normalization – bool.Whether normalize the attention score of local activation unit.

• l2_reg_dnn – float. L2 regularizer strength applied to DNN

• l2_reg_embedding – float. L2 regularizer strength applied to embedding vector

• dnn_dropout – float in [0,1), the probability we will drop out a given DNN coordinate.

• init_std – float,to use as the initialize std of embedding vector

• seed – integer ,to use as random seed.

• task – str, "binary" for binary logloss or "regression" for regression loss

Returns

A Keras model instance.