Publications

Sense-able Lunch Recommendations

Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019, Taipei, Taiwan

Published October 1, 2019

Ishan Gupta, Jennifer Healey, Georgios Theocharous, Georgios Theocharous

An ideal mobile user interface provides users with just the information they want, when they want it. We believe that sensors in the ambient environment can help automatically showcase this information. In this paper, we describe how we inferred users' favorite lunch stations using indoor location trajectories. We had 109 users participate in our study over an eight month period and we were able to predict their lunch station choices with 85% accuracy using a heuristic algorithm. We describe our system, the data we collected and our post-hoc user assessment.

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Research Area:  AI & Machine Learning