deepctr.models.multitask.esmm module


Mincai Lai,

Weichen Shen,

[1] Ma X, Zhao L, Huang G, et al. Entire space multi-task model: An effective approach for estimating post-click conversion rate[C]//The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018.(
deepctr.models.multitask.esmm.ESMM(dnn_feature_columns, tower_dnn_hidden_units=(256, 128, 64), l2_reg_embedding=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', dnn_use_bn=False, task_types=('binary', 'binary'), task_names=('ctr', 'ctcvr'))[source]

Instantiates the Entire Space Multi-Task Model architecture.

  • dnn_feature_columns – An iterable containing all the features used by deep part of the model.
  • tower_dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of task DNN.
  • 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
  • dnn_use_bn – bool. Whether use BatchNormalization before activation or not in DNN
  • task_types – str, indicating the loss of each tasks, "binary" for binary logloss or "regression" for regression loss.
  • task_names – list of str, indicating the predict target of each tasks. default value is [‘ctr’, ‘ctcvr’]

A Keras model instance.