A multigrained preference analysis method for product iterative design incorporating AI-generated review detection
Abstract Online reviews significantly influence consumer purchasing decisions and serve as a vital reference for product improvement. With the surge of generative artificial intelligence (AI) technologies such as ChatGPT, some merchants might exploit them to fabricate deceptive positive reviews, and...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86551-5 |
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| author | Zhaojing Su Mei Yang Qingbo Zhai Kaiyuan Guo Yuexin Huang Yangfan Cong |
| author_facet | Zhaojing Su Mei Yang Qingbo Zhai Kaiyuan Guo Yuexin Huang Yangfan Cong |
| author_sort | Zhaojing Su |
| collection | DOAJ |
| description | Abstract Online reviews significantly influence consumer purchasing decisions and serve as a vital reference for product improvement. With the surge of generative artificial intelligence (AI) technologies such as ChatGPT, some merchants might exploit them to fabricate deceptive positive reviews, and competitors may also fabricate negative reviews to influence the opinions of consumers and designers. Attention must be paid to the trustworthiness of online reviews. In addition, the opinions expressed by users are limited, and design details hidden behind reviews also affect the product usage experience. Therefore, on the basis of integrated AI-generated review detection, a multigrained user preference analysis method is proposed in this work. The proposed method utilizes pre-trained language models and designs an authenticity detection model for online reviews. Subsequently, attribute-grained preference analysis is considered a text-filling problem and uses the text-infilling objective for domain-adaptive pretraining, facilitating knowledge transfer. On the basis of the feature selection algorithm, a calculation method for the importance of product design features is proposed by introducing a random idea. The proposed method analyzes user preferences at the granularity of product attributes and design features, enabling targeted cost control and optimization in product development and guiding design decisions. Rigorous comparative and few-shot experiments substantiate the superiority of the proposed method. |
| format | Article |
| id | doaj-art-794caa702e2b4ae4b7e203da090f8062 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-794caa702e2b4ae4b7e203da090f80622025-01-26T12:23:59ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-025-86551-5A multigrained preference analysis method for product iterative design incorporating AI-generated review detectionZhaojing Su0Mei Yang1Qingbo Zhai2Kaiyuan Guo3Yuexin Huang4Yangfan Cong5Department of Industrial Design, College of Arts, Shandong University of Science and TechnologyDepartment of Industrial Design, College of Arts, Shandong University of Science and TechnologyCollege of Ocean Science and Engineering, Shandong University of Science and TechnologyDepartment of Industrial Design, College of Arts, Shandong University of Science and TechnologyKey Laboratory of Ministry of Industrial Design and Ergonomics, Northwestern Polytechnical UniversityKey Laboratory of Ministry of Industrial Design and Ergonomics, Northwestern Polytechnical UniversityAbstract Online reviews significantly influence consumer purchasing decisions and serve as a vital reference for product improvement. With the surge of generative artificial intelligence (AI) technologies such as ChatGPT, some merchants might exploit them to fabricate deceptive positive reviews, and competitors may also fabricate negative reviews to influence the opinions of consumers and designers. Attention must be paid to the trustworthiness of online reviews. In addition, the opinions expressed by users are limited, and design details hidden behind reviews also affect the product usage experience. Therefore, on the basis of integrated AI-generated review detection, a multigrained user preference analysis method is proposed in this work. The proposed method utilizes pre-trained language models and designs an authenticity detection model for online reviews. Subsequently, attribute-grained preference analysis is considered a text-filling problem and uses the text-infilling objective for domain-adaptive pretraining, facilitating knowledge transfer. On the basis of the feature selection algorithm, a calculation method for the importance of product design features is proposed by introducing a random idea. The proposed method analyzes user preferences at the granularity of product attributes and design features, enabling targeted cost control and optimization in product development and guiding design decisions. Rigorous comparative and few-shot experiments substantiate the superiority of the proposed method.https://doi.org/10.1038/s41598-025-86551-5Product iterative designUser preference analysisAI-generated review detectionUser-generated contentPretrained language modelText filling |
| spellingShingle | Zhaojing Su Mei Yang Qingbo Zhai Kaiyuan Guo Yuexin Huang Yangfan Cong A multigrained preference analysis method for product iterative design incorporating AI-generated review detection Scientific Reports Product iterative design User preference analysis AI-generated review detection User-generated content Pretrained language model Text filling |
| title | A multigrained preference analysis method for product iterative design incorporating AI-generated review detection |
| title_full | A multigrained preference analysis method for product iterative design incorporating AI-generated review detection |
| title_fullStr | A multigrained preference analysis method for product iterative design incorporating AI-generated review detection |
| title_full_unstemmed | A multigrained preference analysis method for product iterative design incorporating AI-generated review detection |
| title_short | A multigrained preference analysis method for product iterative design incorporating AI-generated review detection |
| title_sort | multigrained preference analysis method for product iterative design incorporating ai generated review detection |
| topic | Product iterative design User preference analysis AI-generated review detection User-generated content Pretrained language model Text filling |
| url | https://doi.org/10.1038/s41598-025-86551-5 |
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