Eye Tracking In Recommender Systems Data Skeptic podcast

Content provided by Kyle Polich. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kyle Polich or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Data Skeptic »
Eye Tracking in Recommender Systems

19h ago 52:08

MP3•Episode home

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 patterns. Looking forward, Santiago discusses exciting future directions including simulating user behavior using eye tracking data, addressing the cold start problem, and translating these findings to e-commerce applications. This conversation challenges researchers and practitioners to think more deeply about de-biasing clicks and leveraging eye tracking as a powerful tool to enhance user experience in recommendation systems.

598 episodes

#Data Science #Datamining #Datascience #Machinelearning #Statistics #Kyle Polich #Science #Math #Tech #Skeptic