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)
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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')[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.