Improving target acquisition in Web applications with link prediction

Abstract

Web application users spend considerable time clicking on hyperlinks and buttons to complete frequent tasks. Individual application developers can optimize their interfaces to improve typical usage; however, no single task model will accurately reflect the needs of a wide audience of users. This thesis describes EasyLink, an automated optimization to the view of Web applications. EasyLink facilitates the common activities of individual users without explicit customization by each user. Using a record of the user's actions, EasyLink adapts the view of the page on later visits. The new view reduces unused elements by decreasing their contrast and emphasizes the most used elements by enlarging their size and ease of pointing. An evaluation of EasyLink on Gmail shows that it accurately models 64% of user behaviour, significantly reduces the time to complete simple tasks, and is preferred by users over the default view of Gmail.