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A study of factors affecting the utility of implicit relevance feedback
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Source Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval table of contents
Salvador, Brazil
SESSION: Relevance feedback table of contents
Pages: 35 - 42  
Year of Publication: 2005
ISBN:1-59593-034-5
Authors
Ryen W. White  University of Maryland, College Park, MD
Ian Ruthven  University of Strathclyde, Glasgow, Scotland
Joemon M. Jose  University of Glasgow, Glasgow, Scotland
Sponsor
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 17,   Downloads (12 Months): 99,   Citation Count: 14
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ABSTRACT

Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system. IRF is a new method of gathering information on user interest and, if IRF is to be used in operational IR systems, it is important to establish when it performs well and when it performs poorly. In this paper we investigate how the use and effectiveness of IRF is affected by three factors: search task complexity, the search experience of the user and the stage in the search. Our findings suggest that all three of these factors contribute to the utility of IRF.


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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White, R.W. (2004). Implicit feedback for interactive information retrieval. Unpublished Doctoral Dissertation, University of Glasgow, Glasgow, United Kingdom.
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White, R.W., Jose, J.M., Ruthven, I. and Van Rijsbergen, C.J. (2004). A simulated study of implicit feedback models. Proceedings of the 26th European Conference on Information Retrieval, 311--326.
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CITED BY  14

Collaborative Colleagues:
Ryen W. White: colleagues
Ian Ruthven: colleagues
Joemon M. Jose: colleagues