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Using web-graph distance for relevance feedback in web search

Published: 06 August 2006 Publication History

Abstract

We study the effect of user supplied relevance feedback in improving web search results. Rather than using query refinement or document similarity measures to rerank results, we show that the web-graph distance between two documents is a robust measure of their relative relevancy. We demonstrate how the use of this metric can improve the rankings of result URLs, even when the user only rates one document in the dataset. Our research suggests that such interactive systems can significantly improve search results.

References

[1]
Lars Arge, Gerth Stølting Brodal, and Laura Toma. On external-memory MST, SSSP, and Multi-way Planar Graph Separation. In SWAT, pages 433--447, 2000.]]
[2]
Doug Beeferman and Adam L. Berger. Agglomerative clustering of a search engine query log. In KDD, pages 407--416, 2000.]]
[3]
N. Belkin, C. Cool, J. Koenemann, K. Ng, and S. Park. Using relevance feedback and ranking in interactive searching, 1996.]]
[4]
J. Dean and M. Henzinger. Finding related pages in the World Wide Web, 1999.]]
[5]
Andrew V. Goldberg and Chris Harrelson. Computing the shortest path: search meets graph theory. In SODA, pages 156--165, 2005.]]
[6]
Andrew V. Goldberg, Haim Kaplan, and Chris Harrelson. Reach for A*: Efficient point-to-point shortest path algorithms. In ALENEX, 2006.]]
[7]
Kalervo Jarvelin and Jaana Kekalainen. IR evaluation methods for retrieving highly relevant documents. In SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, pages 41--48, New York, NY, USA, 2000. ACM Press.]]
[8]
Glen Jeh and Jennifer Widom. Scaling personalized web search. In WWW, pages 271--279, 2003.]]
[9]
Jon M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604--632, 1999.]]
[10]
Jurgen Koenemann and Nicholas J. Belkin. A case for interaction: A study of interactive information retrieval behavior and effectiveness. In CHI, pages 205--212, 1996.]]
[11]
Kurt Mehlhorn and Ulrich Meyer. External-memory breadth-first search with sublinear I/O. In ESA, pages 723--735, 2002.]]
[12]
MSN search. http://search.msn.com.]]
[13]
Seda Ozmutlu, Amanda Spink, and Huseyin C. Ozmutlu. A day in the life of web searching: an exploratory study. Inf. Process. Manage., 40(2):319--345, 2004.]]
[14]
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project, 1998.]]
[15]
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, Zheng Chen, and Wei-Ying Ma. A study of relevance propagation for web search. In SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 408--415, New York, NY, USA, 2005. ACM Press.]]
[16]
J. J. Rocchio. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Indexing, pages 324--336. Prentice Hall, Englewood Cliffs, NJ, 1971.]]
[17]
Azadeh Shakery and ChengXiang Zhai. Relevance propagation for topic distillation UIUC TREC 2003 Web Track Experiments. In TREC, pages 673--677, 2003.]]
[18]
Jaime Teevan, Susan T. Dumais, and Eric Horvitz. Personalizing search via automated analysis of interests and activities. In SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 449--456, New York, NY, USA, 2005. ACM Press.]]
[19]
Vishwa Vinay, Kenneth R. Wood, Natasa Milic-Frayling, and Ingemar J. Cox. Comparing relevance feedback algorithms for web search. In Allan Ellis and Tatsuya Hagino, editors, WWW (Special interest tracks and posters), pages 1052--1053. ACM, 2005.]]
[20]
Vivisimo search engine. http://www.vivisimo.com.]]
[21]
Yahoo mindset. http://mindset.research.yahoo.com.]]

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    cover image ACM Conferences
    SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
    August 2006
    768 pages
    ISBN:1595933697
    DOI:10.1145/1148170
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 06 August 2006

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    Author Tags

    1. link analysis
    2. relevance feedback
    3. web search

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    SIGIR06: The 29th Annual International SIGIR Conference
    August 6 - 11, 2006
    Washington, Seattle, USA

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    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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