Mitigating Selection Bias in Recommendation Systems Through Sentiment Analysis and Dynamic Debiasing
Selection bias can cause recommendation systems to over-rely on users’ historical behavior and ignore potential interests, thus reducing the diversity and accuracy of recommendations. Our research on selection bias reveals that the existing literature often overlooks the impact of sentiment factors...
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MDPI AG
2025-04-01
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| author | Fan Zhang Wenjie Luo Xiudan Yang |
| author_facet | Fan Zhang Wenjie Luo Xiudan Yang |
| author_sort | Fan Zhang |
| collection | DOAJ |
| description | Selection bias can cause recommendation systems to over-rely on users’ historical behavior and ignore potential interests, thus reducing the diversity and accuracy of recommendations. Our research on selection bias reveals that the existing literature often overlooks the impact of sentiment factors on selection bias. In recommendation tasks, sentiment bias—stemming from users’ sentiment reactions—can lead to the suggestion of low-quality products to important users and unfair recommendations of niche items (targeted at specific markets or purposes). Addressing sentiment bias and enhancing recommendations for key users could help balance research on selection bias. Sentiment bias is embedded in user ratings and reviews. To mitigate this bias, it is essential to analyze user ratings and comments to uncover genuine sentiments. To this end, we have developed a sentiment analysis module aimed at eliminating discrepancies between reviews and ratings, providing accurate sentiment scores, extracting users’ true opinions, and reducing sentiment bias. Additionally, we have designed a combinatorial function that adapts to three distinct scenarios for bias correction. Moreover, we introduce the concept of dynamic debiasing, where the modeling time is not fixed but varies over time. On this basis, we propose a dynamic selection debiased recommendation method based on sentiment analysis. This paper demonstrates how the three approaches—sentiment analysis for data sparsity, combinatorial functions for dataset optimization, and time-dynamic modeling with inverse propensity weighting—can effectively mitigate selection bias. Our experiments with multiple real-world datasets show that our model can significantly enhance recommendation performance. |
| format | Article |
| id | doaj-art-cd9244e4d442472cb5fec0ce006c8b75 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-cd9244e4d442472cb5fec0ce006c8b752025-08-20T02:28:27ZengMDPI AGApplied Sciences2076-34172025-04-01158417010.3390/app15084170Mitigating Selection Bias in Recommendation Systems Through Sentiment Analysis and Dynamic DebiasingFan Zhang0Wenjie Luo1Xiudan Yang2School of Cyber Security and Computer, Hebei University, Baoding 071000, ChinaSchool of Cyber Security and Computer, Hebei University, Baoding 071000, ChinaSchool of Management, Hebei University, Baoding 071000, ChinaSelection bias can cause recommendation systems to over-rely on users’ historical behavior and ignore potential interests, thus reducing the diversity and accuracy of recommendations. Our research on selection bias reveals that the existing literature often overlooks the impact of sentiment factors on selection bias. In recommendation tasks, sentiment bias—stemming from users’ sentiment reactions—can lead to the suggestion of low-quality products to important users and unfair recommendations of niche items (targeted at specific markets or purposes). Addressing sentiment bias and enhancing recommendations for key users could help balance research on selection bias. Sentiment bias is embedded in user ratings and reviews. To mitigate this bias, it is essential to analyze user ratings and comments to uncover genuine sentiments. To this end, we have developed a sentiment analysis module aimed at eliminating discrepancies between reviews and ratings, providing accurate sentiment scores, extracting users’ true opinions, and reducing sentiment bias. Additionally, we have designed a combinatorial function that adapts to three distinct scenarios for bias correction. Moreover, we introduce the concept of dynamic debiasing, where the modeling time is not fixed but varies over time. On this basis, we propose a dynamic selection debiased recommendation method based on sentiment analysis. This paper demonstrates how the three approaches—sentiment analysis for data sparsity, combinatorial functions for dataset optimization, and time-dynamic modeling with inverse propensity weighting—can effectively mitigate selection bias. Our experiments with multiple real-world datasets show that our model can significantly enhance recommendation performance.https://www.mdpi.com/2076-3417/15/8/4170dynamic debiasing recommendationsselected biassentiment analysisIPS |
| spellingShingle | Fan Zhang Wenjie Luo Xiudan Yang Mitigating Selection Bias in Recommendation Systems Through Sentiment Analysis and Dynamic Debiasing Applied Sciences dynamic debiasing recommendations selected bias sentiment analysis IPS |
| title | Mitigating Selection Bias in Recommendation Systems Through Sentiment Analysis and Dynamic Debiasing |
| title_full | Mitigating Selection Bias in Recommendation Systems Through Sentiment Analysis and Dynamic Debiasing |
| title_fullStr | Mitigating Selection Bias in Recommendation Systems Through Sentiment Analysis and Dynamic Debiasing |
| title_full_unstemmed | Mitigating Selection Bias in Recommendation Systems Through Sentiment Analysis and Dynamic Debiasing |
| title_short | Mitigating Selection Bias in Recommendation Systems Through Sentiment Analysis and Dynamic Debiasing |
| title_sort | mitigating selection bias in recommendation systems through sentiment analysis and dynamic debiasing |
| topic | dynamic debiasing recommendations selected bias sentiment analysis IPS |
| url | https://www.mdpi.com/2076-3417/15/8/4170 |
| work_keys_str_mv | AT fanzhang mitigatingselectionbiasinrecommendationsystemsthroughsentimentanalysisanddynamicdebiasing AT wenjieluo mitigatingselectionbiasinrecommendationsystemsthroughsentimentanalysisanddynamicdebiasing AT xiudanyang mitigatingselectionbiasinrecommendationsystemsthroughsentimentanalysisanddynamicdebiasing |