Source code for deepctr.models.sequence.din

# -*- coding:utf-8 -*-
    Weichen Shen,

    [1] Zhou G, Zhu X, Song C, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1059-1068. (
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense, Concatenate, Flatten

from ...feature_column import SparseFeat, VarLenSparseFeat, DenseFeat, build_input_features
from ...inputs import create_embedding_matrix, embedding_lookup, get_dense_input, varlen_embedding_lookup, \
from ...layers.core import DNN, PredictionLayer
from ...layers.sequence import AttentionSequencePoolingLayer
from ...layers.utils import concat_func, NoMask, combined_dnn_input

[docs]def DIN(dnn_feature_columns, history_feature_list, dnn_use_bn=False, dnn_hidden_units=(256, 128, 64), dnn_activation='relu', att_hidden_size=(80, 40), att_activation="dice", att_weight_normalization=False, l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, seed=1024, task='binary'): """Instantiates the Deep Interest Network architecture. :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. :param history_feature_list: list,to indicate sequence sparse field :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in deep net :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net :param dnn_activation: Activation function to use in deep net :param att_hidden_size: list,list of positive integer , the layer number and units in each layer of attention net :param att_activation: Activation function to use in attention net :param att_weight_normalization: bool.Whether normalize the attention score of local activation unit. :param l2_reg_dnn: float. L2 regularizer strength applied to DNN :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. :param seed: integer ,to use as random seed. :param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss :return: A Keras model instance. """ features = build_input_features(dnn_feature_columns) sparse_feature_columns = list( filter(lambda x: isinstance(x, SparseFeat), dnn_feature_columns)) if dnn_feature_columns else [] dense_feature_columns = list( filter(lambda x: isinstance(x, DenseFeat), dnn_feature_columns)) if dnn_feature_columns else [] varlen_sparse_feature_columns = list( filter(lambda x: isinstance(x, VarLenSparseFeat), dnn_feature_columns)) if dnn_feature_columns else [] history_feature_columns = [] sparse_varlen_feature_columns = [] history_fc_names = list(map(lambda x: "hist_" + x, history_feature_list)) for fc in varlen_sparse_feature_columns: feature_name = if feature_name in history_fc_names: history_feature_columns.append(fc) else: sparse_varlen_feature_columns.append(fc) inputs_list = list(features.values()) embedding_dict = create_embedding_matrix(dnn_feature_columns, l2_reg_embedding, seed, prefix="") query_emb_list = embedding_lookup(embedding_dict, features, sparse_feature_columns, history_feature_list, history_feature_list, to_list=True) keys_emb_list = embedding_lookup(embedding_dict, features, history_feature_columns, history_fc_names, history_fc_names, to_list=True) dnn_input_emb_list = embedding_lookup(embedding_dict, features, sparse_feature_columns, mask_feat_list=history_feature_list, to_list=True) dense_value_list = get_dense_input(features, dense_feature_columns) sequence_embed_dict = varlen_embedding_lookup(embedding_dict, features, sparse_varlen_feature_columns) sequence_embed_list = get_varlen_pooling_list(sequence_embed_dict, features, sparse_varlen_feature_columns, to_list=True) dnn_input_emb_list += sequence_embed_list keys_emb = concat_func(keys_emb_list, mask=True) deep_input_emb = concat_func(dnn_input_emb_list) query_emb = concat_func(query_emb_list, mask=True) hist = AttentionSequencePoolingLayer(att_hidden_size, att_activation, weight_normalization=att_weight_normalization, supports_masking=True)([ query_emb, keys_emb]) deep_input_emb = Concatenate()([NoMask()(deep_input_emb), hist]) deep_input_emb = Flatten()(deep_input_emb) dnn_input = combined_dnn_input([deep_input_emb], dense_value_list) output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input) final_logit = Dense(1, use_bias=False)(output) output = PredictionLayer(task)(final_logit) model = Model(inputs=inputs_list, outputs=output) return model