What you look at is what you get: eye movement-based interaction techniques
CHI '90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 6th international conference on Intelligent user interfaces
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Conversing with the user based on eye-gaze patterns
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Gaze-based interaction for semi-automatic photo cropping
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Personalized online document, image and video recommendation via commodity eye-tracking
Proceedings of the 2008 ACM conference on Recommender systems
Combining eye tracking and conventional techniques for indications of user-adaptability
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
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To reduce the intrusive interaction and workload for the product recommendations, we seek implicit methods to indicate users' preferences and recommend desirable products on the interface automatically. In this paper, we validate our approach with interactive genetic algorithm to compute fitness based on the eye-movement data metrics. And construct the adaptation strategies for content and layout design on the user interface. A digital camera recommendation prototype is proposed, and in the user study, we find that users can get interested products information with less physical effort and more satisfactions.