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| Review of Interpretable Machine Learning for Information Resource Management |
Liu Zhifeng,Wang Jimin( ) |
| Department of Information Management, Peking University, Beijing 100871, China |
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Abstract [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.
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Received: 23 March 2023
Published: 12 September 2023
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| Fund:National Social Science Fund of China(20ATQ007) |
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Corresponding Authors:
Wang Jimin，ORCID：0000-0002-3573-7788，E-mail：wjm@pku.edu.cn。
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