Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Discovering genes-diseases associations from specialized literature using the grid
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Visual attention for implicit relevance feedback in a content based image retrieval
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Journal of Biomedical Informatics
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
Eye-tracking product recommenders' usage
Proceedings of the fourth ACM conference on Recommender systems
Recommender Systems: An Introduction
Recommender Systems: An Introduction
Eye-Tracking study of user behavior in recommender interfaces
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
| Hi-index | 0.00 |
In this work, we present a proactive content based recommender system that employs web document clustering performed by using eye gaze data. Generally, recommender systems are used in commercial applications, where information about the user's habits and interests are of crucial importance in order to plan marketing strategies, or in information retrieval systems in order to suggest similar resources a user is interested in. Commonly, these systems use explicit relevance feedback techniques (e.g. mouse or keyboard) to improve their performance and to recommend products. In contrast, the proposed system permits to capture user's interest by using implicit relevance feedback, based on data acquired by an eye tracker Tobii T60. The purpose of the system is to collect eye gaze data during web navigation and, by employing clustering techniques, to suggest web documents similar to those that the user, implicitly, expressed greater interest. Performance evaluation was carried out on 30 users and the results show that the proposed system enhanced navigation experience in about 73% of the cases.