Supporting command reuse: empirical foundations and principles
International Journal of Man-Machine Studies
Supporting command reuse: mechanisms for reuse
International Journal of Man-Machine Studies
How people revisit web pages: empirical findings and implications for the design of history systems
International Journal of Human-Computer Studies - Special issue: World Wide Web usability
SIGMETRICS '99 Proceedings of the 1999 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Integrating back, history and bookmarks in web browsers
CHI '01 Extended Abstracts on Human Factors in Computing Systems
A comparison of static, adaptive, and adaptable menus
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A predictive model of menu performance
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Ephemeral adaptation: the use of gradual onset to improve menu selection performance
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Revisiting read wear: analysis, design, and evaluation of a footprints scrollbar
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A similarity measure for indefinite rankings
ACM Transactions on Information Systems (TOIS)
Use of information visualization and adaptive hypermedia techniques on content portals
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Improving navigation-based file retrieval
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Skillometers: reflective widgets that motivate and help users to improve performance
Proceedings of the 26th annual ACM symposium on User interface software and technology
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We introduce AccessRank, an algorithm that predicts revisitations and reuse in many contexts, such as file accesses, website visits, window switches, and command lines. AccessRank uses many sources of input to generate its predictions, including recency, frequency, temporal clustering, and time of day. Simulations based on log records of real user interaction across a diverse range of applications show that AccessRank more accurately predicts upcoming accesses than other algorithms. The prediction lists generated by AccessRank are also shown to be more stable than other algorithms that have good predictive capability, which can be important for usability when items are presented in lists as users can rely on their spatial memory for target location. Finally, we present examples of how real world applications might use AccessRank.