Combining eye-tracking technologies with web usage mining for identifying Website Keyobjects

This paper introduces a novel approach for collecting and processing data originated by web user ocular movements on a web page, which are captured by using an eye-tracking tool. These data allow knowing the exact web user's eye position on a computer screen, and by combining them with the sequence of web page visits registered in the web log, significant insights about his/her behavior within a website can be extracted. With this approach, we can improve the effectiveness of the current methodology for identifying the most important web objects from the web user's point of view, also called Website Keyobjects. It takes as input the website's logs, the pages that compose it and the interest of users in the web objects of each page, which is quantified by means of a survey. Subsequently, the data are transformed and preprocessed before finally applying web mining algorithms that allow the extraction of the Website Keyobjects. With the utilization of the eye-tracking technology, we can eliminate the survey by using a more precise and objective tool to achieve an improvement in the classification of the Website Keyobjects. 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 between 15% and 20% when using the information generated by the eye tracker.

[1]  Donna L. Hoffman,et al.  Measuring the Customer Experience in Online Environments: A Structural Modeling Approach , 2000 .

[2]  Meredith Ringel Morris,et al.  What do you see when you're surfing?: using eye tracking to predict salient regions of web pages , 2009, CHI.

[3]  Joel B. Richman,et al.  Best practices for eye tracking of television and video user experiences , 2008, UXTV '08.

[4]  Sankar K. Pal,et al.  Web mining in soft computing framework: relevance, state of the art and future directions , 2002, IEEE Trans. Neural Networks.

[5]  Linden J. Ball,et al.  Eye Tracking in Human-Computer Interaction and Usability Research : Current Status and Future Prospects , 2004 .

[6]  Sean Bechhofer,et al.  Visual complexity and aesthetic perception of web pages , 2008, SIGDOC '08.

[7]  Caroline Jay,et al.  End User Evaluations , 2008, Web Accessibility.

[8]  Mark Shelhamer,et al.  A new "wireless" search-coil system , 2008, ETRA.

[9]  Juan D. Velásquez,et al.  Web site keywords: A methodology for improving gradually the web site text content , 2012, Intell. Data Anal..

[10]  Arie E. Kaufman,et al.  An eye tracking computer user interface , 1993, Proceedings of 1993 IEEE Research Properties in Virtual Reality Symposium.

[11]  M. A. Frens,et al.  Recording eye movements with video-oculography and scleral search coils: a direct comparison of two methods , 2002, Journal of Neuroscience Methods.

[12]  Jakob Nielsen,et al.  Usability engineering , 1997, The Computer Science and Engineering Handbook.

[13]  Kenneth Holmqvist,et al.  Eye tracking: a comprehensive guide to methods and measures , 2011 .

[14]  P. NovakThomas,et al.  Measuring the Customer Experience in Online Environments , 2000 .

[15]  Gerhard Tröster,et al.  Eye Movement Analysis for Activity Recognition Using Electrooculography , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Juan D. Velásquez Silva.  Improvement of a Methodology for Website Keyobject Identification through the Application of Eye-Tracking Technologies , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[17]  Donna L. Hoffman,et al.  A New Marketing Paradigm for Electronic Commerce , 1997, Inf. Soc..

[18]  Jakob Nielsen,et al.  Eyetracking Web Usability , 2009 .

[19]  Tim Berners-Lee,et al.  The World-Wide Web , 1994, CACM.

[20]  V. Palade,et al.  Adaptive Web Sites - A Knowledge Extraction from Web Data Approach , 2008, Frontiers in Artificial Intelligence and Applications.

[21]  Carlos Hitoshi Morimoto,et al.  Pupil detection and tracking using multiple light sources , 2000, Image Vis. Comput..

[22]  James A. M. McHugh,et al.  Mining the World Wide Web , 2001, The Information Retrieval Series.

[23]  Juan D. Velásquez,et al.  Extracting significant Website Key Objects: A Semantic Web mining approach , 2011, Eng. Appl. Artif. Intell..

[24]  Hendrik Blockeel,et al.  Web mining research: a survey , 2000, SKDD.

[25]  Henrik I. Christensen,et al.  Computational visual attention systems and their cognitive foundations: A survey , 2010, TAP.

[26]  Terumasa Aoki,et al.  Towards the Identification of Keywords in the Web Site Text Content: A Methodological Approach , 2005, Int. J. Web Inf. Syst..

[27]  Oren Etzioni,et al.  The World-Wide Web: quagmire or gold mine? , 1996, CACM.

[28]  Andrew T. Duchowski,et al.  Eye Tracking Methodology: Theory and Practice , 2003, Springer London.

[29]  Wei-Na Lee,et al.  A Comparison of Objective Characteristics and User Perception of Web Sites , 2004 .

[30]  Juan D. Velásquez,et al.  Design and Implementation of a Methodology for Identifying Website Keyobjects , 2009, KES.

[31]  Susan T. Dumais,et al.  The good, the bad, and the random: an eye-tracking study of ad quality in web search , 2010, SIGIR.