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Lydia B. Chilton


Publications by Lydia B. Chilton (bibliography)

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Chilton, Lydia B. and Teevan, Jaime (2011): Addressing people's information needs directly in a web search result page. In: Proceedings of the 2011 International Conference on the World Wide Web 2011. pp. 27-36. http://dx.doi.org/10.1145/1963405.1963413

Web search engines have historically focused on connecting people with information resources. For example, if a person wanted to know when their flight to Hyderabad was leaving, a search engine might connect them with the airline where they could find flight status information. However, search engines have recently begun to try to meet people's search needs directly, providing, for example, flight status information in response to queries that include an airline and a flight number. In this paper, we use large scale query log analysis to explore the challenges a search engine faces when trying to meet an information need directly in the search result page. We look at how people's interaction behavior changes when inline content is returned, finding that such content can cannibalize clicks from the algorithmic results. We see that in the absence of interaction behavior, an individual's repeat search behavior can be useful in understanding the content's value. We also discuss some of the ways user behavior can be used to provide insight into when inline answers might better trigger and what types of additional information might be included in the results.

© All rights reserved Chilton and Teevan and/or ACM Press

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Little, Greg, Chilton, Lydia B., Goldman, Max and Miller, Robert C. (2010): TurKit: human computation algorithms on mechanical turk. In: Proceedings of the 2010 ACM Symposium on User Interface Software and Technology 2010. pp. 57-66. http://doi.acm.org/10.1145/1866029.1866040

Mechanical Turk (MTurk) provides an on-demand source of human computation. This provides a tremendous opportunity to explore algorithms which incorporate human computation as a function call. However, various systems challenges make this difficult in practice, and most uses of MTurk post large numbers of independent tasks. TurKit is a toolkit for prototyping and exploring algorithmic human computation, while maintaining a straight-forward imperative programming style. We present the crash-and-rerun programming model that makes TurKit possible, along with a variety of applications for human computation algorithms. We also present case studies of TurKit used for real experiments across different fields.

© All rights reserved Little et al. and/or their publisher

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