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Conference Papers Year : 2017

BIGnav: Bayesian Information Gain for Guiding Multiscale Navigation


This paper introduces BIGnav, a new multiscale navigation technique based on Bayesian Experimental Design where the criterion is to maximize the information-theoretic concept of mutual information, also known as information gain. Rather than simply executing user navigation commands, BIGnav interprets user input to update its knowledge about the user's intended target. Then it navigates to a new view that maximizes the information gain provided by the user's expected subsequent input. We conducted a controlled experiment demonstrating that BIGnav is significantly faster than conventional pan and zoom and requires fewer commands for distant targets, especially in non-uniform information spaces. We also applied BIGnav to a realistic application and showed that users can navigate to highly probable points of interest on a map with only a few steps. We then discuss the tradeoffs of BIGnav—including efficiency vs. increased cognitive load—and its application to other interaction tasks.
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Dates and versions

hal-01677122 , version 1 (08-01-2018)



Wanyu Liu, Rafael Lucas d'Oliveira, Michel Beaudouin-Lafon, Olivier Rioul. BIGnav: Bayesian Information Gain for Guiding Multiscale Navigation. ACM CHI 2017 - International conference of Human-Computer Interaction, May 2017, Denver, United States. pp.5869-5880, ⟨10.1145/3025453.3025524⟩. ⟨hal-01677122⟩
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