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Authordc.contributor.authorVera, Felipe 
Authordc.contributor.authorCortés, Víctor D. 
Authordc.contributor.authorIturrra, Gabriel 
Authordc.contributor.authorVelásquez Silva, Juan 
Authordc.contributor.authorMaldonado Arbogast, Pedro 
Authordc.contributor.authorCouve Correa, Andrés 
Admission datedc.date.accessioned2019-05-29T13:41:24Z
Available datedc.date.available2019-05-29T13:41:24Z
Publication datedc.date.issued2017
Cita de ítemdc.identifier.citationIEEE International Conference on Data Mining Workshops, ICDMW, Volumen 2017-November
Identifierdc.identifier.issn23759259
Identifierdc.identifier.issn23759232
Identifierdc.identifier.other10.1109/ICDMW.2017.90
Identifierdc.identifier.urihttps://repositorio.uchile.cl/handle/2250/169133
Abstractdc.description.abstractAs the use of the Internet grows every year, ecommerce’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.
Lenguagedc.language.isoen
Publisherdc.publisherIEEE
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
Link to Licensedc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
Sourcedc.sourceIEEE International Conference on Data Mining Workshops, ICDMW
Keywordsdc.subjectWeb Preferences
Keywordsdc.subjectWeb Usage Mining
Keywordsdc.subjectWebsite Key Objects
Títulodc.titleAkori: A tool based in eye-tracking techniques for analyzing web user behaviour on a web site
Document typedc.typeArtículo de revista
Catalogueruchile.catalogadorlaj
Indexationuchile.indexArtículo de publicación SCOPUS
uchile.cosechauchile.cosechaSI


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Attribution-NonCommercial-NoDerivs 3.0 Chile
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile