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A Survey of Eigenvector Methods for Web Information Retrieval

Langville, Amy N., Meyer, Carl D.·2005·SIAM Review

Abstract

Web information retrieval is significantly more challenging than traditional well-controlled, small document collection information retrieval. One main difference between traditional information retrieval and Web information retrieval is the Web's hyperlink structure. This structure has been exploited by several of today's leading Web search engines, particularly Google and Teoma. In this survey paper, we focus on Web information retrieval methods that use eigenvector computations, presenting the three popular methods of HITS, PageRank, and SALSA.

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Metadata

Authors
Langville, A. N., & Meyer, C. D.
Venue
SIAM Review
Published
2005-01
Volume
47
Issue
1
Pages
135-161
ISSN
  • 0036-1445 (ISSN-L)
  • 1095-7200
OpenAlex
W2145541974
Cited by
430
References
6
FWCI
90.91
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Survey of Eigenvector Methods for Web Information Retrieval. https://app.cochat.ai/papers/10.1137/s0036144503424786
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Langville, A. N., & Meyer, C. D. (2005). A Survey of Eigenvector Methods for Web Information Retrieval. SIAM Review. https://doi.org/10.1137/s0036144503424786

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.