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Tuesday, 28 July 2009 11:06

Bing could be improved by game

Written by Nick Farell

Image

Search and destroy


Microsoft's
new search engine, Bing, could be improved at some point through the use of an online game called Page Hunt.

Boffins at Microsoft Research have released a new paper which suggests that the game, called Page Hunt, could be used to refine queries and search results. Bing currently sits in third in the U.S. search-engine market behind Yahoo and Google, even as Microsoft pumps between $80 million and $100 million into the initial marketing. Page Hunt, provides users with a random Web page, and then asks them to input the search terms that will put that page within a search engine’s top five search results.

Depending on how close to the top of the rankings their queries put the Web page, players are awarded points. In order to sweeten the experience, the game adds animations, a top-score list, bonus points, and other "game-like" features. Page Hunt can be found on this site is based around Redmond's Silverlight and it exists entirely as a research project, with no direct connection to Bing.

The results it generates could contribute mightily to the extraordinarily complex task of refining the search-engine process. Microsoft needs to do this if it is going to get any advantage over Google and Yahoo in the online search space. According to the report, which has the catchy title "Page Hunt: Improving Search Engines Using Human Computation Games" was written by Raman Chandrasekar and Chris Quirk of Microsoft Research, with Abhishek Gupta of Digital Media and Hao Ma of the Chinese University of Hong Kong. Using data from users gathered in human computation games can improve search, the abstract of the paper reads.

The original pilot experiment conducted by Chandrasekar and company involved 341 Microsoft employees playing Page Hunt over a 10-day period, generating 14,400 labels for the 744 Web pages in the system. The researchers extracted the queries that corresponded to winning trials, generated all pairs of queries as bitext data, and applied the bitext matching algorithm.

Nick Farell

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