Cascade: crowdsourcing taxonomy creation

  • Authors:

  • Lydia B. Chilton;Greg Little;Darren Edge;Daniel S. Weld;James A. Landay

  • Affiliations:

  • University of Washington, Seattle, Washington, USA;oDesk, Redwood City, California, USA;Microsoft Research Asia, Beijing, Beijing, China;University of Washington, Seattle, Washington, USA;University of Washington, Seattle, Washington, USA

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2013

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Abstract

Taxonomies are a useful and ubiquitous way of organizing information. However, creating organizational hierarchies is difficult because the process requires a global understanding of the objects to be categorized. Usually one is created by an individual or a small group of people working together for hours or even days. Unfortunately, this centralized approach does not work well for the large, quickly changing datasets found on the web. Cascade is an automated workflow that allows crowd workers to spend as little at 20 seconds each while collectively making a taxonomy. We evaluate Cascade and show that on three datasets its quality is 80-90% of that of experts. Cascade has a competitive cost to expert information architects, despite taking six times more human labor. Fortunately, this labor can be parallelized such that Cascade will run in as fast as four minutes instead of hours or days.