Source code for deepctr.models.multitask.esmm

    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.(

from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense, Multiply

from ...feature_column import build_input_features, input_from_feature_columns
from ...layers.core import PredictionLayer, DNN
from ...layers.utils import combined_dnn_input

[docs]def ESMM(dnn_feature_columns, tower_dnn_hidden_units=(256, 128, 64), l2_reg_embedding=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', dnn_use_bn=False, task_types=('binary', 'binary'), task_names=('ctr', 'ctcvr')): """Instantiates the Entire Space Multi-Task Model architecture. :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. :param tower_dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of task DNN. :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_types: str, indicating the loss of each tasks, ``"binary"`` for binary logloss or ``"regression"`` for regression loss. :param task_names: list of str, indicating the predict target of each tasks. default value is ['ctr', 'ctcvr'] :return: A Keras model instance. """ if len(task_names) != 2: raise ValueError("the length of task_names must be equal to 2") for task_type in task_types: if task_type != 'binary': raise ValueError("task must be binary in ESMM, {} is illegal".format(task_type)) features = build_input_features(dnn_feature_columns) inputs_list = list(features.values()) sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, seed) dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list) ctr_output = DNN(tower_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)( dnn_input) cvr_output = DNN(tower_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)( dnn_input) ctr_logit = Dense(1, use_bias=False)(ctr_output) cvr_logit = Dense(1, use_bias=False)(cvr_output) ctr_pred = PredictionLayer('binary', name=task_names[0])(ctr_logit) cvr_pred = PredictionLayer('binary')(cvr_logit) ctcvr_pred = Multiply(name=task_names[1])([ctr_pred, cvr_pred]) # CTCVR = CTR * CVR model = Model(inputs=inputs_list, outputs=[ctr_pred, ctcvr_pred]) return model