构建首个结合眼动与鼠标的搜索结果页行为数据集，助力精准分析用户注意力。
A Versatile Dataset of Mouse and Eye Movements on Search Engine Results Pages
- 用眼动追踪获取连续视觉注意力的客观标注，弥补自报数据偏差。
- 涵盖47人2776次搜索查询，含眼动、鼠标、广告框等多模态数据。
- 适合研究用户注意力、点击行为或人机交互的学者使用。
我们贡献了一个全面的数据集，用于研究用户在搜索引擎结果页（SERPs）上的注意力与购买行为。以往研究依赖鼠动作为低成本大规模行为代理，但常依赖任务后自报的真实标签，存在不准确和偏差问题。为解决此局限，我们采用眼动仪构建连续视觉注意力的客观真实标签。数据集包含47名参与者在谷歌搜索结果页上完成的2776次交易型查询，包含：(1) HTML源文件（含CSS与图片）；(2) 渲染后的搜索结果截图；(3) 眼动数据；(4) 鼠标移动数据；(5) 直接展示与自然排名广告的边界框；(6) 数据预处理脚本。本文概述该数据集并提供基线分类实验，以启发未来研究方向。
原文摘要 · Abstract (English)
We contribute a comprehensive dataset to study user attention and purchasing behavior on Search Engine Result Pages (SERPs). Previous work has relied on mouse movements as a low-cost large-scale behavioral proxy but also has relied on self-reported ground-truth labels, collected at post-task, which can be inaccurate and prone to biases. To address this limitation, we use an eye tracker to construct an objective ground-truth of continuous visual attention. Our dataset comprises 2,776 transactional queries on Google SERPs, collected from 47 participants, and includes: (1) HTML source files, with CSS and images; (2) rendered SERP screenshots; (3) eye movement data; (4) mouse movement data; (5) bounding boxes of direct display and organic advertisements; and (6) scripts for further preprocessing the data. In this paper we provide an overview of the dataset and baseline experiments (classification tasks) that can inspire researchers about the different possibilities for future work.
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。