基于用户交互的阅读区域预测数据集

本数据集由明尼苏达大学双城分校的研究团队创建，包含20万条基于眼动追踪技术收集的真实阅读行为数据。数据集涵盖了参与者在浏览器上阅读新闻时的眼动数据，用于训练和验证机器学习模型，以预测用户对不同消息区域的阅读时间和阅读深度。该数据集的应用领域包括内容个性化和推荐系统，旨在帮助平台更好地理解用户兴趣并优化内容展示。

This dataset was developed by a research team from the University of Minnesota Twin Cities, and contains 200,000 real-world reading behavior samples collected through eye-tracking technology. It covers eye-tracking data of participants while they read news on web browsers, and is designed for training and validating machine learning models to predict users' reading duration and reading depth on different message regions. The application scenarios of this dataset include content personalization and recommendation systems, aiming to help platforms better understand user interests and optimize content presentation.