Liu Yuqiao, Zhang Shuai
Art and Design College, Shenyang Ligong University, Shenyang, China.
PLoS One. 2025 May 22;20(5):e0321477. doi: 10.1371/journal.pone.0321477. eCollection 2025.
User interaction for product selection relies on its design and technical support to improve the quality of the experience. Decision support systems are incorporated to leverage user experience through product interactions. This article introduces an interaction-based fuzzy decision support (FDS) system to meet user demands in product design through suggestions for user interaction. The proposed system models the maximum possible interaction features through previous user experiences and reviews. Based on these two factors, the fuzzy decisions for interaction improvement or product design modification are identified through likelihood. This likelihood is a variant between lower and higher fuzzy combinations for maximum interaction pursued by the user. The fuzzy process develops multiple higher-order recommendation variants from the interaction computed to improve the user experience. The lower-order variants recommend different product design features to increase the interaction rate. Thus, the decision process determines the need for adaptability through interactive platforms to achieve a better experience. This methodology aimed to improve the interaction rate of 97.4% with better impacts on product design and modification using likelihood variants. The user experience assessment is performed using the higher-order variants with a better user adaptability rate of 98.9%, maximizing the recommendations.
产品选择中的用户交互依赖于其设计和技术支持来提升体验质量。决策支持系统被整合进来，以通过产品交互来利用用户体验。本文介绍了一种基于交互的模糊决策支持（FDS）系统，通过对用户交互的建议来满足产品设计中的用户需求。所提出的系统通过先前的用户体验和评价来对最大可能的交互特征进行建模。基于这两个因素，通过可能性来确定交互改进或产品设计修改的模糊决策。这种可能性是用户所追求的最大交互的较低和较高模糊组合之间的一个变量。模糊过程从计算出的交互中开发出多个高阶推荐变量，以改善用户体验。低阶变量推荐不同的产品设计特征以提高交互率。因此，决策过程通过交互式平台确定适应性需求，以实现更好的体验。该方法旨在通过可能性变量将交互率提高97.4%，对产品设计和修改产生更好的影响。使用高阶变量进行用户体验评估，用户适应性率达到98.9%，使推荐最大化。
