Benjamin Korvemaker and Russell Greiner, University of Alberta
As every user has his own idiosyncrasies and preferences, an interface that is honed for one user may be problematic for another. To accommodate a diverse range of users, many computer applications therefore include an interface that can be customized --- e.g., by adjusting parameters, or defining macros. This allows each user to have his ``own'' version of the interface, honed to his specific preferences. However, most such interfaces require the user to perform this customization by hand -- a tedious process that requires the user to be aware of his personal preferences. We are therefore exploring adaptive interfaces, that can autonomously determine the user’s preference, and adjust the interface appropriately. This paper describes such an adaptive system --- here a UNIX-shell that can predict the user’s next command, and then use this prediction to simplify the user’s future interactions. These predictions are determined by combining the distributions learned by a set of relatively simple experts, each using its own type of information. In a series of experiments, on real-world data, we demonstrate that this system can correctly predict the user’s next command almost 50% of the time, and can do so robustly -- across a range of different users.