Eye Tracking Methodology: Theory and Practice
Eye Tracking Methodology: Theory and Practice
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Best practices for eye tracking of television and video user experiences
Proceedings of the 1st international conference on Designing interactive user experiences for TV and video
Design and Implementation of a Methodology for Identifying Website Keyobjects
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Eyetracking Web Usability
Advanced Techniques in Web Intelligence -1
Advanced Techniques in Web Intelligence -1
Extracting significant Website Key Objects: A Semantic Web mining approach
Engineering Applications of Artificial Intelligence
Web mining in soft computing framework: relevance, state of the art and future directions
IEEE Transactions on Neural Networks
Combining eye-tracking technologies with web usage mining for identifying Website Keyobjects
Engineering Applications of Artificial Intelligence
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This paper introduces the utilization of data originated in the web user ocular movement to improve the methodology for identifying Website Key objects that was designed by Velasquez and Dujovne through the use of eye tracking tools. Given a website, this methodology takes as input the request register (Web log) of the website, the pages that compose it and the interest of users in the web objects of each page, which is quantified using a survey. Subsequently, the data is transformed and preprocessed before finally applying Web mining algorithms that allow the extraction of the Website Key objects. In this paper, a novel application of the eye tracking technology is proposed, in order to dispense with the survey, that is to say, using a more precise tool to achieve an improvement in the classification of the Website Key objects. It was concluded that eye tracking technology is useful and accurate when it comes to knowing what a user looks at and therefore, what attracts their attention the most. Finally, it was established that there is an improvement of between 15% and 16% when using the information generated by the eye tracker.