About this episode
Santiago de Leon discusses eye tracking technology and its applications in recommender systems.
In this episode, Santiago de Leon takes us deep into the world of eye tracking and its revolutionary applications in recommender systems. As a researcher at the Kempelin Institute and Brno University, Santiago explains the mechanics of eye tracking technology—how it captures gaze data and processes it into fixations and saccades to reveal user browsing patterns. He introduces the groundbreaking RecGaze dataset, the first eye tracking dataset specifically designed for recommender systems research, which opens new possibilities for understanding how users interact with carousel interfaces like Netflix. Through collaboration between psychologists and AI researchers, Santiago's work demonstrates how eye tracking can uncover insights about positional bias and user engagement that traditional click data misses. Beyond the technical aspects, Santiago addresses the ethical considerations surrounding eye tracking data, particularly concerning pupil data and privacy. He emphasizes the importance of questioning assumptions in recommender systems and shares practical advice for improving recommendation algorithms by understanding actual user behavior rather than relying solely on click…
People in this episode
Host: Kyle Polich
Guest: Santiago de Leon
Topics covered
- eye tracking
- recommender systems
- user behavior
- data privacy
- AI research
Keywords
- eye tracking
- recommender systems
- RecGaze dataset
- user engagement
- positional bias
Mentioned in this episode
Organizations: Kempelin Institute, Brno University, Netflix
Products: RecGaze dataset
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