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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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http://www.gravity.com/blog/snow-fight-skiing-versus-snowboarding/, Gravity. com 2. 17. 2015. Web 6. 1. 2016. |
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E. R. Tufte,
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C. Ware,
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N. Yau,
Visualize This John Wiley & Sons, Indianapolis, Indiana, 2011.
doi: 10.1002/9781118722213. |
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J. Yu and H. Cooper,
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doi: 10.1103/PhysRevE.76.046115. |
show all references
References:
[1] |
A. Cairo,
The Functional Art: An Introduction to Information Graphics and Visualization New Riders, Berkeley, CA, 2013. |
[2] |
M. Bostock,
D3. js -Data-Driven Documents Available from: https://d3js.org/. |
[3] |
L. Kozlowski, Gravity can graph your internet clicks for new customer snapshots,
http://www.forbes.com/sites/lorikozlowski/2013/11/13/brand-graphs-a-new-snapshot-of-consumers/#7adbf713a2d1, forbes. com 11. 13. 2013. Web 6. 1. 2016. |
[4] |
I. Krumpal,
Determinants of social desirability bias in sensitive surveys: A literature review, Qual. Quant., 47 (2013), 2025-2047.
doi: 10.1007/s11135-011-9640-9. |
[5] |
V. Kumar, Building a Taste Graph: The basic principles,
http://bigdata-madesimple.com/category/tech-and-tools/analytics/, bigdata-madesimple. com 12. 23. 2014. Web 6. 1. 2016. |
[6] |
S. Pearman, Delicious interest graphs: Taco bell and whole foods,
http://www.gravity.com/blog/delicious-interest-graphs-taco-bell-and-whole-foods/, Gravity. com 8. 12. 2013. Web 6. 1. 2016. |
[7] |
S. Pearman, What Your Electric Car Says About You,
http://www.gravity.com/blog/what-your-electric-car-says-about-you/, Gravity. com 7. 31. 2013. Web 6. 15. 2016. |
[8] |
B. Shneiderman,
The eyes have it: A task by data type taxonomy for information visualizations, IEEE Symposium on Visual Languages Proceedings, (1996), 336-343.
doi: 10.1109/VL.1996.545307. |
[9] |
A. Taylor, Snow Fight: Skiing versus Snowboarding,
http://www.gravity.com/blog/snow-fight-skiing-versus-snowboarding/, Gravity. com 2. 17. 2015. Web 6. 1. 2016. |
[10] |
E. R. Tufte,
The Visual Display of Quantitative Information 2nd edition, Graphics Press, Cheshire, Conn., 2001. |
[11] |
C. Ware,
Information Visualization: Perception for Design Elsevier, Waltham, MA, 2013. |
[12] |
N. Yau,
Visualize This John Wiley & Sons, Indianapolis, Indiana, 2011.
doi: 10.1002/9781118722213. |
[13] |
J. Yu and H. Cooper,
A quantitative review of research design effects on response rates to questionnaires, J. Mark. Res., 20 (1983), 36-44.
doi: 10.2307/3151410. |
[14] |
T. Zhou, J. Ren, M. Medo and Y. -C. Zhang, Bipartite network projection and personal recommendation Phys. Rev. E 76 (2007), 046115.
doi: 10.1103/PhysRevE.76.046115. |







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