Gaze prediction for recommender systems

Qian Zhao, Shuo Chang, F. Maxwell Harper, Joseph A. Konstan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

62 Scopus citations


As users browse a recommender system, they systematically consider or skip over much of the displayed content. It seems obvious that these eye gaze patterns contain a rich signal concerning these users' preferences. However, because eye tracking data is not available to most recommender systems, these signals are not widely incorporated into personalization models. In this work, we show that it is possible to predict gaze by combining easily-collected user browsing data with eye tracking data from a small number of users in a grid-based recommender interface. Our technique is able to leverage a small amount of eye tracking data to infer gaze patterns for other users. We evaluate our prediction models in MovieLens - an online movie recommender system. Our results show that incorporating eye tracking data from a small number of users significantly boosts accuracy as compared with only using browsing data, even though the eye-tracked users are different from the testing users (e.g. AUC=0.823 vs. 0.693 in predicting whether a user will fixate on an item). We also demonstrate that Hidden Markov Models (HMMs) can be applied in this setting; they are better than linear models in predicting fixation probability and capturing the interface regularity through Bayesian inference (AUC=0.823 vs. 0.757).

Original languageEnglish (US)
Title of host publicationRecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Number of pages8
ISBN (Electronic)9781450340359
StatePublished - Sep 7 2016
Event10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States
Duration: Sep 15 2016Sep 19 2016

Publication series

NameRecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems


Other10th ACM Conference on Recommender Systems, RecSys 2016
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This work was supported by the National Science Foundation under grant IIS-1319382, and by Google under a Social Computing Focused Research Award.

Publisher Copyright:
© 2016 ACM.


  • Eye tracking
  • Grid-based interface
  • Hidden Markov Models


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