Towards Measuring and Inferring User Interest from Gaze

How can we reliably infer web users’ interest and evaluate the content relevance when lacking active user interaction such as click behavior? In this paper, we investigate the relationship between mobile users’ implicit interest inferred from attention metrics, such as eye gaze or viewport time, and explicit interest expressed by users. We present the first quantitative gaze tracking study using front-facing camera of mobile devices instead of specialized, expensive eye-tracking devices. We focus on multi-column digital media pages in Google Play Store that display 30+ items per page belonging to diverse categories. In such pages, we find significantly different distribution of gaze metrics on items that users rate as interesting vs. not. We leverage this insight by building a prediction model that is able to infer a user’s interest ratings from the the non-click actions of the user. Our model is able to attain AUC of 90.32% in predicting user interest at an individual item level. In addition, our experiments on collection item re-ranking show how user gaze and viewport signals can be used to personalize item ranking on the collection page. Beyond understanding users’ attention behavior in novel contexts such as multi-column digital media pages in Google Play Store, the findings in this study have implications for the design of a novel personalization and recommendation mechanism by (1) prioritizing items that are most likely of interest to users based on historical attention signals, and (2) prioritizing positions receiving significant portion of gaze attention.