Adam Fourney
Richard Mann
ABSTRACT
In this paper, we show how Internet search query logs can yield rich, ecologically valid data sets describing the common tasks and issues that people encounter when using software on a day-to-day basis. These data sets can feed directly into standard usability practices. We address challenges in collecting, filtering, and summarizing queries, and show how data can be collected at very low cost, even without direct access to raw query logs.
- }}Ziv Bar-Yossef and Maxim Gurevich. Mining search engine query logs via suggestion sampling. Proc. VLDB Endow., 1(1):54--65, 2008. Google Scholar
Digital Library
- }}Joel Brandt, Philip J. Guo, Joel Lewenstein, Mira Dontcheva, and Scott R. Klemmer. Two studies of opportunistic programming: interleaving web foraging, learning, and writing code. In Proc CHI '09, pages 1589--1598, New York, NY, 2009. ACM. Google ScholarDigital Library
- }}Hang Cui, Ji-Rong Wen, Jian-Yun Nie, and Wei-Ying Ma. Probabilistic query expansion using query logs. In Proc WWW '02, pages 325--332, New York, NY, USA, 2002. ACM. Google ScholarDigital Library
- }}Melanie Kellar, Carolyn Watters, and Michael Shepherd. A field study characterizing web-based information-seeking tasks. J. Am. Soc. Inf. Sci. Technol., 58(7):999--1018, 2007. Google ScholarDigital Library
- }}W. Quesenbery, C. Jarrett, I. Roddis, S. Allen, and V. Stirling. Search Is Now Normal Behavior. What Do We Do about That. In UPA 2008, Baltimore, Maryland, USA, June 2008.Google Scholar
- }}Matthew Richardson. Learning about the world through long-term query logs. ACM Trans. Web, 2(4):1--27, 2008. Google ScholarDigital Library
Index Terms
- What can internet search engines "suggest" about the usage and usability of popular desktop applications?
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