NeTS: Small: Improving Web User Experience Using Eye Gaze

NeTS：小型：使用 Eye Gaze 改善 Web 用户体验

• 批准号: 1718014
• 负责人: Aruna Balasubramanian
• 金额: $ 49.53万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718014&HistoricalAwards=false
• 关键词: NeTS; Small; Improving; Web; User

Web page access is one of the most popular applications on the global Internet, and is a key element of the digital engine that drives the economy. While the technologies that support Web applications are well-developed and ubiquitous, mechanisms for high quality user experience have not been built into their development. (As one example, almost every user has experienced delays in accessing content while ads download.) Existing techniques for Web optimization focus on ensuring fast downloads, but research has shown that this has poor correlation with good user experience. Further, download speeds are limited by network conditions; network congestion or low access network bandwidth can make them painfully slow. This project aims to address this problem by developing techniques for Web page optimization that significantly improve user experience by downloading the content of value to the user first. This project's primary goal is to ensure that objects on a Web page are loaded in the order in which the user consumes them, thus significantly improving user experience. The key idea is to use user's eye gaze as a signal for user's consumption of Web objects (or alternately the user interest). The project will develop novel techniques to use gaze signal to prioritize Web page loads, using commercial eye-tracking technology, and leveraging the researchers' experience in eye-tracking in other applications. The fundamental contributions of this project are to (1) Develop a utility-driven optimization framework to dynamically reprioritize Web page loads based on the user eye gaze, object sizes, and network conditions; (2) Remove the human-in-the-loop by building high-level saliency models of eye gaze based on the characteristics of the Web page. The gaze tracking will only be used infrequently, to seed and update the model. (3) Design a large scale crowdsourced technique to track user gaze to drive the optimization and modeling; and (4) Evaluate the optimization and modeling specifically in terms of user experience by conducting large scale, crowdsourced, user evaluation.

网页访问是全球互联网上最受欢迎的应用之一，也是推动经济的数字引擎的关键要素。 虽然支持Web应用程序的技术已经得到了很好的开发，并且无处不在，但是高质量用户体验的机制还没有内置到它们的开发中。(As例如，几乎每个用户在下载广告时都经历过访问内容的延迟。 现有的Web优化技术侧重于确保快速下载，但研究表明这与良好的用户体验相关性较差。 此外，下载速度受到网络条件的限制;网络拥塞或低接入网络带宽可能会使它们非常慢。该项目旨在通过开发网页优化技术来解决这个问题，这些技术通过首先将有价值的内容下载给用户来显着改善用户体验。 该项目的主要目标是确保网页上的对象按照用户使用它们的顺序加载，从而显着改善用户体验。其关键思想是使用用户的眼睛注视作为用户消费Web对象（或用户兴趣）的信号。该项目将开发新的技术，使用凝视信号来优先考虑网页加载，使用商业眼动跟踪技术，并利用研究人员在其他应用中的眼动跟踪经验。该项目的基本贡献是：（1）开发一个实用程序驱动的优化框架，根据用户的眼睛注视，对象大小和网络条件动态地重新确定Web页面负载的优先级;（2）通过建立基于Web页面特征的眼睛注视的高级显着性模型来消除人在回路中。视线跟踪将仅偶尔用于种子和更新模型。 (3)设计大规模众包技术来跟踪用户注视以驱动优化和建模;以及（4）通过进行大规模众包用户评估，具体地在用户体验方面评估优化和建模。

项目成果

Reading Detection in Real-time

实时读取检测

• 发表时间: 2019
• 期刊: Symposium on Eye Tracking Research And Application
• 作者: Kelton, Conor;Wei, Zijun;Ahn, Seoyoung;Balasubramanian, Aruna;Das, Samir R;Samaras, Dimitris;Zelinsky, Gregory
• 通讯作者: Zelinsky, Gregory