School of Design, Jiangnan University, Jiangsu, Wuxi 214122, China.
Graduate School Design, Dongseo University, Jurye-Ro, Busan 47011, Republic of Korea.
Comput Intell Neurosci. 2022 Apr 28;2022:2096634. doi: 10.1155/2022/2096634. eCollection 2022.
With the development of virtual reality and digital reconstruction technology, digital museums have been widely promoted in various cities. Digital museums offer new ways to display and disseminate cultural heritage. It allows remote users to autonomously browse displays in a physical museum environment in a digital space. It is also possible to reproduce the lost heritage through digital reconstruction and restoration, so as to digitally present tangible cultural heritage and intangible cultural heritage to the public. However, the user's experience of using digital museums has not been fully and deeply studied at present. In this study, the user's experience evaluation data of digital museum are classified and processed, so as to analyze the user's emotional trend towards the museum. Considering that the user's evaluation data are unbalanced data, this study uses an unbalanced support vector machine (USVM) in the classification of user evaluation data. The main idea of this method is that the boundary of the support vector is continuously shifted to the majority class by repeatedly oversampling some support vectors until the real support vector samples are found. The experimental results show that the classification obtained by the used USVM has a good practical reference value. Based on the classification results of the evaluation data, the construction of the digital museum can be further guided and maintained, thereby improving the user experience satisfaction of the museum. This research will make an important contribution to the construction of the museum and the inheritance of culture.
随着虚拟现实和数字重建技术的发展，数字博物馆在各大城市得到了广泛的推广。数字博物馆提供了展示和传播文化遗产的新途径。它允许远程用户在数字空间中自主浏览物理博物馆环境中的展品。也可以通过数字重建和修复来再现失去的遗产，从而将有形文化遗产和无形文化遗产数字化呈现给公众。然而，目前对于数字博物馆用户体验的研究还不够全面和深入。在本研究中，对数字博物馆用户体验评价数据进行分类和处理，以分析用户对博物馆的情感趋势。考虑到用户的评价数据是不平衡数据，本研究在用户评价数据的分类中使用了不平衡支持向量机（USVM）。该方法的主要思想是通过反复对一些支持向量进行过采样，不断将支持向量的边界向多数类移动，直到找到真正的支持向量样本。实验结果表明，所使用的 USVM 的分类具有良好的实际参考价值。基于评价数据的分类结果，可以进一步指导和维护数字博物馆的建设，从而提高博物馆用户体验的满意度。这项研究将对博物馆的建设和文化的传承做出重要贡献。
