Page Hunt: using human computation games to improve web search.

KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Paris France June, 2009(2009)

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摘要
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.
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