NeTS: Small: Improving Web User Experience Using Eye Gaze

网页访问是全球互联网上最受欢迎的应用之一，也是推动经济增长的数字引擎的关键要素。虽然支持Web应用程序的技术已经开发得很好，而且无处不在，但它们的开发中并没有构建高质量用户体验的机制。(举个例子，几乎每个用户在下载广告时都会遇到访问内容的延迟。)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很差。此外，下载速度受到网络条件的限制；网络拥塞或访问网络带宽低可能会使下载速度非常慢。该项目旨在通过开发网页优化技术来解决这一问题，该技术通过首先将有价值的内容下载给用户来显著改善用户体验。该项目的主要目标是确保Web页面上的对象按照用户使用它们的顺序加载，从而显著改善用户体验。其关键思想是使用用户的眼睛凝视作为用户对Web对象的消费(或者用户兴趣)的信号。该项目将开发新的技术，使用凝视信号来确定网页加载的优先顺序，使用商业眼球跟踪技术，并利用研究人员在其他应用中的眼球跟踪经验。该项目的基本贡献是(1)开发一个效用驱动的优化框架，根据用户的眼睛注视、对象大小和网络条件动态地重新排序网页负载的优先顺序；(2)通过根据网页的特征建立高级别的眼睛注视显著模型来消除人在循环中的影响。凝视跟踪只会很少使用，用于设定种子和更新模型。(3)设计一种大规模的众包技术来跟踪用户的凝视，以驱动优化和建模；(4)通过进行大规模的众包用户评估，从用户体验的角度对优化和建模进行具体评估。

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.