deepctr.models.dien module

Author:
Weichen Shen,wcshen1994@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.dien.DIEN(dnn_feature_columns, history_feature_list, gru_type='GRU', use_negsampling=False, alpha=1.0, use_bn=False, dnn_hidden_units=(200, 80), 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, init_std=0.0001, seed=1024, task='binary')[source]

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.