一种用于产品代设计的多谷类偏好分析方法,采用人工智能生成的审查检测
Zhaojing Su1,2, Mei Yang3, Qingbo Zhai4
1Department of Industrial Design, College of Arts, Shandong University of Science and Technology, Tsingtao, 266590, China.
Scientific reports
|January 20, 2025
概括
这项研究引入了一种新的方法来检测虚假的在线评论,并分析用户在属性和设计功能层面的偏好. 它通过确保审查真实性和优化设计选择来增强产品开发.
科学领域:
- 人工智能的人工智能
- 消费者行为 消费者行为
- 产品设计 产品设计
背景情况:
- 在线评论极大地影响购买决策和产品开发.
- 生成型人工智能可以制造假评论,损害可信度.
- 用户的意见有限,掩盖了影响产品体验的关键设计细节.
研究的目的:
- 开发一种用于检测人工智能生成的假评论的方法.
- 分析用户在属性和设计特征细节方面的偏好.
- 通过真实的审查分析和有针对性的优化指导产品开发.
主要方法:
- 利用预先训练的语言模型用于人工智能生成的审查检测模型.
- 处理属性粒度偏好分析作为域自适应预训的文本填充问题.
- 引入了一个具有随机想法的特征选择算法来计算产品设计特征的重要性.
主要成果:
- 开发了一个强大的AI生成的审查检测模型.
- 启用了多粒度用户偏好分析 (属性和设计特征).
- 通过比较和少数镜头实验,证明了对现有方法的优越性.
结论:
- 拟议的方法提高了在线评论的可信度.
- 能够在产品开发中实现有针对性的成本控制和优化.
- 根据用户偏好,为产品设计决策提供数据驱动的指导.
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