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Geographic features in web search retrieval
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Workshop On Geographic Information Retrieval archive
Proceeding of the 2nd international workshop on Geographic information retrieval table of contents
Napa Valley, California, USA
SESSION: Query methods table of contents
Pages 57-58  
Year of Publication: 2008
ISBN:978-1-60558-253-5
Authors
Rosie Jones  Yahoo! Labs, Burbank, CA, USA
Ahmed Hassan  U. Michigan Ann Arbor, Ann Arbor, MI, USA
Fernando Diaz  Yahoo! Labs, Montreal, PQ, Canada
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

We conduct large-scale search engine relevance experiments, using the 12% of queries that contain placenames, matching the placenames to places in the documents, and examining the impact of geographic features on web retrieval relevance. Specifically we examine distance between query and document place-names mentioned, noting that when a document has multiple places (which we observe in 82% of documents) we must choose a function over those multiple places. We find that the minimum distance between the document locations and query location is the strongest geographical predictor of document relevance, and that combining geographic features with text features gives us a 5% improvement in relevance over using text features alone.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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J. H. Friedman. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5):1189--1232, 2001.
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Y. Li, N. Stokes, L. Cavedon, and A. Moffat. Nicta i2d2 group at geoclef 2006. In CLEF, pages 938--945, 2006.
 
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Collaborative Colleagues:
Rosie Jones: colleagues
Ahmed Hassan: colleagues
Fernando Diaz: colleagues