Abstract
With the increase in the number of menu items and the menu structure complexity, users have to spend more time in locating menu items when using menu-based interfaces. Recently, adaptive menu techniques have been explored to reduce the time and menu item prediction plays a crucial role in the techniques. Unfortunately, there still lacks effective prediction models for menu items. This chapter per the authors explores the potential of three prediction models based on Markov chain in predicting top n menu items with human behavior data while interacting with menus - the users' historical menu item selections. The results show that Weighted Markov Chain using Genetic Algorithm can obtain the highest prediction accuracy and significantly decrease navigation time by 22.6% when N equals 4 as compared to the static counterpart. Two application scenarios of these models on mobile devices and desktop also demonstrated the potentials in daily usage to reduce the time spent to search target menu items.
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