Towards Measuring and Inferring User Interest from Gaze

Towards Measuring and Inferring User Interest from Gaze
H/T Dawn Anderson

Rethinking the page.

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.

Read the article:
http://www.cs.cornell.edu/~yli/papers/www17-cr.pdf