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 language||English (US)|
|Title of host publication||RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||8|
|State||Published - Sep 7 2016|
|Event||10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States|
Duration: Sep 15 2016 → Sep 19 2016
|Name||RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems|
|Other||10th ACM Conference on Recommender Systems, RecSys 2016|
|Period||9/15/16 → 9/19/16|
Bibliographical noteFunding Information:
This work was supported by the National Science Foundation under grant IIS-1319382, and by Google under a Social Computing Focused Research Award.
© 2016 ACM.
- Eye tracking
- Grid-based interface
- Hidden Markov Models