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Getting work done on the web: supporting transactional queries
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Source Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval table of contents
Seattle, Washington, USA
SESSION: Web IR: current topics table of contents
Pages: 557 - 564  
Year of Publication: 2006
ISBN:1-59593-369-7
Authors
Yunyao Li  University of Michigan, Ann Arbor, MI
Rajasekar Krishnamurthy  IBM Almaden Research Center, San Jose, CA
Shivakumar Vaithyanathan  IBM Almaden Research Center, San Jose, CA
H. V. Jagadish  University of Michigan, Ann Arbor, MI
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Many searches on the web have a transactional intent. We argue that pages satisfying transactional needs can be distinguished from the more common pages that have some information and links, but cannot be used to execute a transaction. Based on this hypothesis, we provide a recipe for constructing a transaction annotator. By constructing an annotator with one corpus and then demonstrating its classification performance on another,we establish its robustness. Finally, we show experimentally that a search procedure that exploits such pre-annotation greatly outperforms traditional search for retrieving transactional pages.


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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D. Hawking et al. Which search engine is best at finding online services? In WWW, 2000.
 
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I.-H. Kang. Transactional query identification in web search. In AIRS, 2005.
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Lucene: http://lucene.apache.org/java/docs/.
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T. Upstill et al. Buying bestsellers online: A case study in search & searchability. In ADCS, 2002.
 
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T. Westerveld et al. Retrieving web pages using content, links, urls and anchors. In TREC, 2001.
 
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Wget: http://www.gnu.org/software/wget/wget.html.
 
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Wordnet: http://wordnet.princeton.edu/.
 
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H. Zhu et al. Topic learning from few examples. In PKDD, 2003.


Collaborative Colleagues:
Yunyao Li: colleagues
Rajasekar Krishnamurthy: colleagues
Shivakumar Vaithyanathan: colleagues
H. V. Jagadish: colleagues