Akori: A Tool Based in Eye-Tracking Techniques for Analyzing Web User Behaviour on a Web Site

As the use of the Internet grows every year, e-commerce's usage does as well. There is a tough competition between companies to be able to attract customers to use their services. The design of a website is crucial to retain a customer, and a retained client is more valuable over time, so understanding what attracts the attention of a potential client on a website is really important. This work proposes a novel web platform for understanding the most important features of a website for the user, based on biometric information provided by eye-trackers and electroencephalogram. Akori platform offers three services for understanding the most important part of a web page to the user. The first is the visual attention map, which highlights in different colors the most attractive zones for the user. The second service is a visual attention map too, but it uses a grey-scale gradient instead of colors. The third service, uses the salience map to identify the Website Key Objects on a web page and highlight the objects that are predicted as such. Our platform is useful to the telecommunication and advertising industries, as interviews with companies managers reveal. Thus, Akori promises to be a fundamental part for planning website design.

[1]  Juan D. Velásquez,et al.  Combining eye-tracking technologies with web usage mining for identifying Website Keyobjects , 2013, Eng. Appl. Artif. Intell..

[2]  Qi Zhao,et al.  SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[3]  Giovanna Castellano,et al.  Web Usage Mining: Discovering Usage Patterns for Web Applications , 2013 .

[4]  Rita Cucchiara,et al.  A deep multi-level network for saliency prediction , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[5]  C. Bulley,et al.  Neuromarketing and the Potential Application of Scientific Methods in Measuring Consumer Behaviour , 2016 .

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

[7]  SCI Facts and Figures 2015 , 2015, The journal of spinal cord medicine.

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

[9]  Nicolas Riche,et al.  Saliency and Human Fixations: State-of-the-Art and Study of Comparison Metrics , 2013, 2013 IEEE International Conference on Computer Vision.

[10]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[11]  Terumasa Aoki,et al.  Mining Web data to create online navigation recommendations , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[12]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[13]  Juan D. Velásquez,et al.  Biometric information fusion for web user navigation and preferences analysis: An overview , 2017, Inf. Fusion.

[14]  Juan D. Velásquez,et al.  Combining eye tracking and pupillary dilation analysis to identify Website Key Objects , 2015, Neurocomputing.

[15]  Peter König,et al.  Measures and Limits of Models of Fixation Selection , 2011, PloS one.

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

[17]  Yuanzhen Li,et al.  Measuring visual clutter. , 2007, Journal of vision.

[18]  Ali Borji,et al.  State-of-the-Art in Visual Attention Modeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Dianne Cyr,et al.  Return visits: a review of how Web site design can engender visitor loyalty , 2014, J. Inf. Technol..

[20]  Juan D. Velásquez,et al.  Eye Tracking and EEG Features for Salient Web Object Identification , 2015, BIH.

[21]  Michael Dorr,et al.  Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  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.

[23]  Juan D. Velásquez,et al.  Web User Click Intention Prediction by Using Pupil Dilation Analysis , 2015, 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT).