Popularity Prediction for Social Media over Arbitrary Time Horizons
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
ACM Symposium on Eye Tracking Research & Applications (ETRA)
Eye gaze promises to be a fast and intuitive way of interacting with technology. Importantly, the performance of a gaze selection paradigm depends on the eye tracker used: Higher tracking accuracy allows for selection of smaller targets, and higher precision and sampling rate allow for faster and more robust interaction. Here we present a novel approach to predict the minimal eye tracker specifications required for gaze-based selection. We quantified selection performance for targets of different sizes while recording high-fidelity gaze data. Selection performance across target sizes was well modeled by a sigmoid similar to a psychometric function. We then simulated lower tracker fidelity by adding noise, a constant spatial bias, or temporal sub-sampling of the recorded data while re-fitting the model each time. Our approach can inform design by predicting performance for a given interface element and tracker fidelity or the minimal element size for a specific performance level.
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Max Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim Hazelwood
Liqi Yan, Qifan Wang, Yiming Cu, Fuli Feng, Xiaojun Quan, Xiangyu Zhang, Dongfang Liu