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Rendering website traffic data into interactive taste graph visualizations

OCAD University 100 McCaul Street Toronto, Ontario M5T 1W1, Canada

We present a method by which to convert a large corpus of website traffic data into interactive and practical taste graph visualizations. The website traffic data lists individual visitors' level of interest in specific pages across the website; it is a tripartite list consisting of anonymized visitor ID, webpage ID, and a score that quantifies interest level. Taste graph visualizations reveal psychological profiles by revealing connections between consumer tastes; for example, an individual with a taste for A may be also have a taste for B. We describe here the method by which we map the web traffic data into a form that can be displayed as interactive taste graphs, and we describe design strategies for communicating the revealed information. In the context of the publishing industry, this interactive visualization is a tool that renders the large corpus of website traffic data into a form that is actionable for marketers and advertising professionals. It could equally be used as a method to personalize services in the domains of government services, education or health and wellness.
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Keywords MapReduce; taste graphs; data visualization; website traffic data; preferences

Citation: Ana Jofre, Lan-Xi Dong, Ha Phuong Vu, Steve Szigeti, Sara Diamond. Rendering website traffic data into interactive taste graph visualizations. Big Data and Information Analytics, 2017, 2(2): 107-118. doi: 10.3934/bdia.2017003


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Copyright Info: 2017, Ana Jofre, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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