Happiness Prediction With Domain Knowledge Integration and Explanation Consistency
Article
Wu, Xiaohua, Li, Lin, Tao, Xiaohui, Xing, Frank and Yuan, Jingling. 2025. "Happiness Prediction With Domain Knowledge Integration and Explanation Consistency." IEEE Transactions on Computational Social Systems. https://doi.org/10.1109/TCSS.2025.3529946
| Article Title | Happiness Prediction With Domain Knowledge Integration and Explanation Consistency |
|---|---|
| ERA Journal ID | 212762 |
| Article Category | Article |
| Authors | Wu, Xiaohua, Li, Lin, Tao, Xiaohui, Xing, Frank and Yuan, Jingling |
| Journal Title | IEEE Transactions on Computational Social Systems |
| Number of Pages | 14 |
| Year | 2025 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | United States |
| ISSN | 2329-924X |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/TCSS.2025.3529946 |
| Web Address (URL) | https://ieeexplore.ieee.org/document/10878303 |
| Abstract | Happiness prediction based on large-scale online data and machine learning models is an emerging research topic that underpins a range of issues, from personal growth to social stability. Many advanced machine learning (ML) models with explanations are used for happiness online assessment while maintaining high accuracy of results. However, expert feedback and sociological theory may be absent from these models, which limits the association between prediction results and the right reasons for why they occurred. Sociological studies have shown that primary and secondary relations are inherent in happiness factors, which can be used as domain knowledge to guide model training. Inspired by such insights, this article attempts to provide new insights into the explanation consistency from an empirical study perspective. Then this article studies how to represent and introduce domain knowledge constraints to make ML models more trustworthy. We achieve this by 1) proving that multiple prediction models with additive factor attributions will have the desirable property of primary and secondary relations consistency; and 2) showing that factor relations with quantity can be represented as an importance distribution for encoding domain knowledge. Factor explanation difference is penalized by the Wasserstein distance among prediction models. Experimental results using two online datasets show that domain knowledge of stable factor relations exists. Using this knowledge not only improves happiness prediction accuracy but also reveals more significant happiness factors for assisting decisions. |
| Keywords | Domain knowledge; explanation consistency; happiness prediction; primary and secondary relations |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 460206. Knowledge representation and reasoning |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Wuhan University of Technology, China |
| Queensland University of Technology | |
| University of Southern Queensland | |
| National University of Singapore |
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https://research.usq.edu.au/item/zx0zz/happiness-prediction-with-domain-knowledge-integration-and-explanation-consistency
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