可解释机器学习在信息资源管理领域的应用研究综述  

Data Analysis and Knowledge Discovery Review of Interpretable Machine Learning for Information Resource Management

作　　者：刘智锋 王继民[1] Liu Zhifeng;Wang Jimin(Department of Information Management,Peking University,Beijing 100871,China)

机构地区：[1]北京大学信息管理系,北京100871

出　　处：《数据分析与知识发现》2024年第1期16-29,共14页Data Analysis and Knowledge Discovery

基　　金：国家社会科学基金重点项目(项目编号:20ATQ007)的研究成果之一。

摘　　要：【目的】对可解释机器学习方法及其在信息资源管理领域的应用研究进行梳理和总结,发现不足并做出展望。【文献范围】使用可解释机器学习的相关关键词构建检索式,在中国知网和Web of Science等平台中检索,根据纳入排除标准,共获取44篇相关文献进行评述。【方法】从机器学习流程出发,构建一般性的可解释机器学习框架,重点梳理可解释机器学习方法分类,然后对可解释机器学习在信息资源管理领域的应用现状进行归纳总结。【结果】一般性的可解释机器学习框架包含事前解释、可解释模型以及事后解释三个不同的模块;事后可解释方法在健康信息学、网络舆情、科学计量学以及社交网络用户行为等领域具有广泛的应用,其中常用的方法为SHAP和特征重要性分析;现有研究存在应用方法单一和融合不足、因果关系探究不够、针对多源异构数据的解释不足以及领域应用有待拓宽等问题。【局限】本文重点关注可解释机器学习的应用及存在的不足,未对算法原理进行深入阐述。【结论】未来研究应加强可解释机器学习方法的融合使用,探究基于因果机器学习的可解释机器学习,引入面向多源异构数据的可解释机器学习方法,拓宽在信息推荐、信息检索和信息计量等多个领域的应用。[Objective]This paper systematically summarizes the research on interpretable machine learning methods and their applications for information resource management.It identifies possible areas of improvements,and provides insights for future research.[Coverage]We searched interpretable machine learning papers from CNKI and Web of Science.A total of 44 related articles were retrieved for review.[Methods]First,from the machine learning process,we constructed a general interpretable machine learning framework.Then,we thoroughly reviewed the classification of interpretable machine learning methods.Finally,we discussed the interpretable machine learning applications for information resource management.[Results]The general interpretable machine learning framework consists of three different modules:pre-explanation,explainable models,and post-explanation.Post-explanation methods have been widely applied in health informatics,online public opinion,scientometrics,and social network user behavior,with the help of commonly used methods including SHAP and feature importance analysis.Many existing research are lack of diversity and integration in applied methods,insufficient exploration of causal relationships,inadequate explanations for multi-source heterogeneous data,and the need for broadening domain applications.[Limitations]This review focuses on the applications and shortcomings of interpretable machine learning.It does not delve into the algorithm principles.[Conclusions]In future research,efforts should be made to strengthen the integration of interpretable machine learning methods,explore interpretable machine learning based on causal machine learning,introduce interpretable machine learning methods for multi-source heterogeneous data.We should also broaden applications in various domains such as information recommendation,information retrieval,and informetrics.

关 键 词：可解释机器学习 SHAP 信息资源管理 研究进展 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]