deepctr.models.edcn module

Author:

Yi He, heyi_jack@163.com

Reference:

[1] Chen, B., Wang, Y., Liu, et al. Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models. CIKM, 2021, October (https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf)

deepctr.models.edcn.EDCN(linear_feature_columns, dnn_feature_columns, cross_num=2, cross_parameterization='vector', bridge_type='concatenation', tau=1.0, l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_cross=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_use_bn=False, dnn_activation='relu', task='binary')[source]

Instantiates the Enhanced Deep&Cross Network architecture.

Parameters

  • linear_feature_columns – An iterable containing all the features used by linear part of the model.

  • dnn_feature_columns – An iterable containing all the features used by deep part of the model.

  • cross_num – positive integet,cross layer number

  • cross_parameterization – str, "vector" or "matrix", how to parameterize the cross network.

  • bridge_type – The type of bridge interaction, one of "pointwise_addition", "hadamard_product", "concatenation" , "attention_pooling"

  • tau – Positive float, the temperature coefficient to control distribution of field-wise gating unit

  • l2_reg_linear – float. L2 regularizer strength applied to linear part

  • l2_reg_embedding – float. L2 regularizer strength applied to embedding vector

  • l2_reg_cross – float. L2 regularizer strength applied to cross net

  • l2_reg_dnn – float. L2 regularizer strength applied to DNN

  • seed – integer ,to use as random seed.

  • 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 DNN

  • dnn_activation – Activation function to use in DNN

  • task – str, "binary" for binary logloss or "regression" for regression loss

Returns

A Keras model instance.