deepctr.models.sequence.bst module¶
- Author:
- Zichao Li, 2843656167@qq.com
- Reference:
- Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, and Wenwu Ou. 2019. Behavior sequence transformer for e-commerce recommendation in Alibaba. In Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP-KDD ‘19). Association for Computing Machinery, New York, NY, USA, Article 12, 1–4. DOI:https://doi.org/10.1145/3326937.3341261
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deepctr.models.sequence.bst.
BST
(dnn_feature_columns, history_feature_list, transformer_num=1, att_head_num=8, use_bn=False, dnn_hidden_units=(256, 128, 64), dnn_activation='relu', l2_reg_dnn=0, l2_reg_embedding=1e-06, dnn_dropout=0.0, seed=1024, task='binary')[source]¶ Instantiates the BST 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.
- transformer_num – int, the number of transformer layer.
- att_head_num – int, the number of heads in multi-head self attention.
- 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
- 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
Returns: A Keras model instance.