deepctr.models.fibinet module

Author:

Weichen Shen, weichenswc@163.com

Reference:

[1] Huang T, Zhang Z, Zhang J. FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.09433, 2019.

deepctr.models.fibinet.FiBiNET(linear_feature_columns, dnn_feature_columns, bilinear_type='interaction', reduction_ratio=3, dnn_hidden_units=(256, 128, 64), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', task='binary')[source]

Instantiates the Feature Importance and Bilinear feature Interaction 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.

  • bilinear_type – str,bilinear function type used in Bilinear Interaction Layer,can be 'all' , 'each' or 'interaction'

  • reduction_ratio – integer in [1,inf), reduction ratio used in SENET Layer

  • dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of DNN

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

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

  • 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_activation – Activation function to use in DNN

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

Returns

A Keras model instance.