Page Hunt: using human computation games to improve web search

Authors:
Hao Ma;Raman Chandrasekar;Chris Quirk;Abhishek Gupta
Affiliations:
The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;Microsoft Research, Redmond, WA;Microsoft Research, Redmond, WA;Georgia Institute of Technology, Atlanta, GA
Venue:
Proceedings of the ACM SIGKDD Workshop on Human Computation
Year:
2009

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Games with a Purpose

Computer
Designing games with a purpose

Communications of the ACM - Designing games with a purpose
Page hunt: improving search engines using human computation games

Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

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Abstract

There has been a lot of work on evaluating and improving the relevance of web search engines, primarily using human relevance judgments or using clickthrough data. Both of these approaches look at the problem of learning the mapping from queries to web pages. In contrast, Page Hunt is a single-player human computation game which seeks to learn a mapping from web pages to queries. In particular, Page Hunt is used to elicit data from players about web pages that can be used to improve search. The data that we elicit from players has several applications including providing metadata for pages, providing query alterations for use in query refinement, and identifying ranking issues. The demo has features which make the game fun, while eliciting useful data.