deepctr.models.dsin module

Author:
Weichen Shen,wcshen1994@163.com
Reference:
[1] Feng Y, Lv F, Shen W, et al. Deep Session Interest Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.06482, 2019.(https://arxiv.org/abs/1905.06482)
deepctr.models.dsin.DSIN(dnn_feature_columns, sess_feature_list, sess_max_count=5, bias_encoding=False, att_embedding_size=1, att_head_num=8, dnn_hidden_units=(200, 80), dnn_activation='sigmoid', dnn_dropout=0, dnn_use_bn=False, l2_reg_dnn=0, l2_reg_embedding=1e-06, init_std=0.0001, seed=1024, task='binary')[source]

Instantiates the Deep Session Interest Network architecture.

Parameters:
  • dnn_feature_columns – An iterable containing all the features used by deep part of the model.
  • sess_feature_list – list,to indicate sequence sparse field
  • sess_max_count – positive int, to indicate the max number of sessions
  • sess_len_max – positive int, to indicate the max length of each session
  • bias_encoding – bool. Whether use bias encoding or postional encoding
  • att_embedding_size – positive int, the embedding size of each attention head
  • att_head_num – positive int, the number of attention head
  • 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
  • dnn_dropout – float in [0,1), the probability we will drop out a given DNN coordinate.
  • dnn_use_bn – bool. Whether use BatchNormalization before activation or not in deep net
  • l2_reg_dnn – float. L2 regularizer strength applied to DNN
  • l2_reg_embedding – float. L2 regularizer strength applied to embedding vector
  • 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.