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The Linear Algebra Behind Search Engines - References and Resources

Amy Langville

Print and Online References

  • Baeza-Yates, R., and B. Ribeiro-Neto (1999). Modern Information Retrieval. New York: ACM Press.
  • Berry, M. W. (Ed.) (2001). Computational Information Retrieval, Proceedings of CIR'00, Philadelphia: SIAM.
  • Berry, M. W., and M. Browne (1999). Understanding Search Engines: Mathematical Modeling and Text Retrieval. Philadelphia, PA: SIAM.
  • Berry, M. W., Z. Drmac, and E. R. Jessup (1999). Matrices, Vector Spaces and Information Retrieval. SIAM Review 41:335-362.
  • Berry, M. W., and G. W. O'Brien (1998). Using linear algebra for intelligent information retrieval. SIAM Review 37:573-595.
  • Books in Print (2001). New York: R.R. Bowker.
  • Bradley, P. (2003). Multi-search engines - a comparison. Web page: Accessed 10/28/05.
  • Ding, C. H. Q. (1999). A similarity-based probability model for LSI. Proceedings of the 22nd ACM SIGIR `99 Conference, pp. 59-65.
  • Dumais, S. T. (1991). Improving the retrieval of information from external sources. Behavior Research Methods, Instruments and Computers 23:229-236.
  • Eckart, C., and G. Young (1936). The approximation of one matrix by another of lower rank. Psychometrika 1:211-218.
  • Frakes, W. B., and R. Baeza-Yates (1992). Information Retrieval: Data Structures and Algorithms. Englewood Cliffs, NJ: Prentice Hall.
  • Glossbrenner, A. and E. (2001). Search Engines for the World Wide Web. Berkeley, CA: Peachpit Press.
  • Golub, G. H., and C. F. Van Loan (1996). Matrix Computations. Baltimore: Johns Hopkins University Press.
  • Harman, D., and E. Voorhees (Eds.) (1996). Overview of the fifth Text REtrieval Conference (TREC-5). In Information Technology: The Fifth Text REtrieval Conference (TREC-5), Gaithersburg, MD: NIST, 500-238 (Nov.): 1-28.
  • Jones, S. K. (1972). A statistical interpretation of term specificity and its applications in retrieval. J. Documentation 28:11-21.
  • Jones, W., and G. Furnas (1987). Pictures of relevance: A geometric analysis of similarity measures. Journal of American Society for Information Retrieval 38:420-442.
  • Korfhage, R. R. (1997). Information Storage and Retrieval. New York: Wiley Computer Publishing.
  • Langville, A. N., and C. D. Meyer (2005). A survey of eigenvector methods for Web information retrieval. SIAM Review, 47(1):135-161.
  • Langville, A. N., and C. D. Meyer (2006). Google's PageRank and Beyond: The Science of Search Engine Rankings. Princeton, NJ: Princeton University Press.
  • Letsche, T. A., and M. W. Berry. (1997). Large-scale information retrieval with LSI. Informatics and Computer Science, 100:105-137.
  • Lyman, P., and H. R. Varian (2000). How Much Information. Web page: Accessed 10/28/05.
  • Marchiori, M. (1997). The quest for correct information of the Web: hyper search engines. The Sixth International WWW Conference (WWW97). Santa Clara, USA, April 7-11.
  • Medlars Collection (2002). Available at Accessed 10/28/05.
  • Meyer, C.D. (2000). Matrix Analysis, Philadelphia: SIAM.
  • Salton, G. (1971). The SMART Retrieval System: Experiments in Automatic Document Processing, New Jersey: Prentice Hall.
  • Ulrich's International Periodicals Directory (2001). New York: R.R. Bowker.
  • Wurman, R. (1989). Information Anxiety, New York: Doubleday.
  • Zha, H., O. Marques, and H. Simon (1998). A subspace-based model for information retrieval with applications in latent semantic indexing. Proceedings of Irregular '98, Lecture Notes in Computer Science, 1457:29-42.
  • Zha, H., and H. D. Simon (1999). On updating problems in latent semantic indexing. SIAM Journal on Scientific Computing, 21(2):782-791.

Online Resources


Journal of Online Mathematics and its Applications