Personalized online document, image and video recommendation via commodity eye-tracking

Abstract

We propose a new recommendation algorithm for online documents, images and videos, which is personalized. Our idea is to rely on the attention time of individual users captured through commodity eye-tracking as the essential clue. The prediction of user interest over a certain online item (a document, image or video) is based on the user's attention time acquired using vision-based commodity eye-tracking during his previous reading, browsing or video watching sessions over the same type of online materials. After acquiring a user's attention times over a collection of online materials, our algorithm can predict the user's probable attention time over a new online item through data mining. Based on our proposed algorithm, we have developed a new online content recommender system for documents, images and videos. The recommendation results produced by our algorithm are evaluated by comparing with those manually labeled by users as well as by commercial search engines including Google (Web) Search, Google Image Search and YouTube. © 2008 ACM.