deepctr.models.sequence.din module

Weichen Shen,
[1] Zhou G, Zhu X, Song C, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1059-1068. (
deepctr.models.sequence.din.DIN(dnn_feature_columns, history_feature_list, dnn_use_bn=False, dnn_hidden_units=(256, 128, 64), dnn_activation='relu', att_hidden_size=(80, 40), att_activation='dice', att_weight_normalization=False, l2_reg_dnn=0, l2_reg_embedding=1e-06, dnn_dropout=0, seed=1024, task='binary')[source]

Instantiates the Deep Interest Network architecture.

  • 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
  • dnn_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 deep net
  • dnn_activation – Activation function to use in deep net
  • att_hidden_size – 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.
  • seed – integer ,to use as random seed.
  • task – str, "binary" for binary logloss or "regression" for regression loss

A Keras model instance.