基于细粒度混杂平衡的营销效果评估方法

郑佳碧; 杨振国; 刘文印

doi:10.12052/gdutxb.210056

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基于细粒度混杂平衡的营销效果评估方法

综合研究 | 更新时间：2026-05-27

- 基于细粒度混杂平衡的营销效果评估方法
- Marketing-Effect Estimation Based on Fine-grained Confounder Balancing
- 广东工业大学学报 2022年39卷第2期页码：55-61
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 鹏城实验室网络空间安全研究中心,广东,深圳,518066
- 作者简介：
  
  郑佳碧(1982–)，女，博士，主要研究方向为因果关系、用户行为，E-mail：zhengjiabi@gmail.com
  刘文印(1966–)，男，教授，博士，主要研究方向为网络身份安全、大数据分析、计算机视觉、机器人、文本挖掘，E-mail：liuwy@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(62076073);广东省基础与应用基础研究基金资助项目(2020A1515010616);广东省创新科研团队计划项目(2014ZT05G157)
- DOI：10.12052/gdutxb.210056
  中图分类号： TP391
- 纸质出版：2022
- 稿件说明：
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郑佳碧, 杨振国, 刘文印. 基于细粒度混杂平衡的营销效果评估方法[J]. 广东工业大学学报, 2022, 39(2): 55-61.

Zheng Jia-bi, Yang Zhen-guo, Liu Wen-yin. Marketing-Effect Estimation Based on Fine-grained Confounder Balancing[J]. 2022, 39(2): 55-61.
郑佳碧, 杨振国, 刘文印. 基于细粒度混杂平衡的营销效果评估方法[J]. 广东工业大学学报, 2022, 39(2): 55-61. DOI： 10.12052/gdutxb.210056.

Zheng Jia-bi, Yang Zhen-guo, Liu Wen-yin. Marketing-Effect Estimation Based on Fine-grained Confounder Balancing[J]. 2022, 39(2): 55-61. DOI： 10.12052/gdutxb.210056.

摘要

营销效果评估是精准化市场营销的重要依据。虽然因果效应估计为营销效果评估提供了有效的研究框架，但现有因果效应估计方法主要针对群体因果效应评估问题而设计，在基于细粒度个体的因果效应评估时往往面临着用户时序特征描述困难、时序非时序混合特征混杂因子选择稳定性差等问题。针对上述问题，提出了细粒度混杂平衡的营销效果评估方法。首先，引入长短记忆神经网络对用户时序特征进行建模；然后，采用稀疏多层神经网络从时序和非时序属性等混杂因子中学习样本权重；最后，采用上阶段学习到的样本权重对营销效果进行独立评估。实验结果验证了本文提出的时序特征建模和近邻匹配思想可降低效果评估的偏差、提升稳定性，对于营销策略优化具有参考意义。

Abstract

The marketing effect estimation is an important issue for precision marketing. Although the causal effect estimation has provided the research framework for this problem

the existing method mainly focuses on the coarse-grained causal effect estimation on a group of users. When dealing with the fine-grained evaluation problem on an individual user

the existing methods usually fail to solve the challenges raised by the user temporal feature modeling and high dimensional confounder selection. To address the above challenges

a fine-grained deep confounder balancing marketing effect estimation method is proposed. Firstly

a long and short memory network is introduced to model the users’ temporal features. Secondly

a neural network with multi-sparse-connected layers is devised to construct the confounders balancing weight network for both temporal and non-temporal features. Finally

the marketing effect is evaluated using the balancing weight learned in the previous stage.It shows that the time feature modeling and nearest neighbor matching ideas proposed in this paper can reduce the deviation of the effect estimation and improve the stability

it has reference significance for the optimization of marketing strategy.

关键词

Keywords

references

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