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, 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
- seed – integer ,to use as random seed.
- task – str,
"binary"
for binary logloss or"regression"
for regression loss
Returns: A Keras model instance.