| Autor: |
Ana Jofre, Lan-Xi Dong, Ha Phuong Vu, Sara Diamond, Steve Szigeti |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
Big Data & Information Analytics. 2:107-118 |
| ISSN: |
2380-6974 |
| DOI: |
10.3934/bdia.2017003 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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