deepctr.models.fwfm module

Author:

Harshit Pande

Reference:

[1] Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising (https://arxiv.org/pdf/1806.03514.pdf)

deepctr.models.fwfm.FwFM(linear_feature_columns, dnn_feature_columns, fm_group=('default_group',), dnn_hidden_units=(256, 128, 64), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_field_strength=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', dnn_use_bn=False, task='binary')[source]

Instantiates the FwFM 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.

  • fm_group – list, group_name of features that will be used to do feature interactions.

  • dnn_hidden_units – list,list of positive integer or empty list if do not want DNN, the layer number and units

in each layer of DNN :param l2_reg_linear: float. L2 regularizer strength applied to linear part :param l2_reg_field_strength: float. L2 regularizer strength applied to the field pair strength parameters :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param l2_reg_dnn: float. L2 regularizer strength applied to DNN :param seed: integer ,to use as random seed. :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. :param dnn_activation: Activation function to use in DNN :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN :param task: str, "binary" for binary logloss or "regression" for regression loss :return: A Keras model instance.