构建首个融合行为与身份的博物馆访客数据集，助力个性化导览推荐。
BIRD: A Museum Open Dataset Combining Behavior Patterns and Identity Types to Better Model Visitors' Experience
- 通过眼动仪采集51名访客在3层博物馆的轨迹与注视数据
- 整合人口统计、动机偏好等10类信息，构建多维度数据集
- 适合文化遗产智能推荐、人机交互研究者使用
人工智能发展受限于数据缺乏，尤其在文化遗产领域，公开数据集稀少且多为特定模型评估所需而采集，难以全面建模访客体验。为此，我们于2019年2月至3月开展研究，为51名参与者配备眼动追踪眼镜，使其自由探索三层博物馆，平均停留57分钟，浏览400余件艺术品。基于此，构建了一个开放数据集，融合上下文数据（人口统计、偏好、参观习惯、动机、社交背景等）、行为数据（时空轨迹、注视数据）及反馈数据（满意度、疲劳度、喜爱作品、原始评论等）。分析揭示了可复现文献中主流访客身份类型，并重现了Veron与Levasseur的典型画像。该数据集将推动博物馆个性化导览优化，实现根据访客兴趣动态调整景点数量、停留时长与信息密度。
原文摘要 · Abstract (English)
Lack of data is a recurring problem in Artificial Intelligence, as it is essential for training and validating models. This is particularly true in the field of cultural heritage, where the number of open datasets is relatively limited and where the data collected does not always allow for holistic modeling of visitors' experience due to the fact that data are ad hoc (i.e. restricted to the sole characteristics required for the evaluation of a specific model). To overcome this lack, we conducted a study between February and March 2019 aimed at obtaining comprehensive and detailed information about visitors, their visit experience and their feedback. We equipped 51 participants with eye-tracking glasses, leaving them free to explore the 3 floors of the museum for an average of 57 minutes, and to discover an exhibition of more than 400 artworks. On this basis, we built an open dataset combining contextual data (demographic data, preferences, visiting habits, motivations, social context. . . ), behavioral data (spatiotemporal trajectories, gaze data) and feedback (satisfaction, fatigue, liked artworks, verbatim. . . ). Our analysis made it possible to re-enact visitor identities combining the majority of characteristics found in the literature and to reproduce the Veron and Levasseur profiles. This dataset will ultimately make it possible to improve the quality of recommended paths in museums by personalizing the number of points of interest (POIs), the time spent at these different POIs, and the amount of information to be provided to each visitor based on their level of interest.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。