Skip to main content
SpringerLink
Log in
Book cover

Encyclopedia of Database Systems pp 4366–4372Cite as

Vector-Space Model

  • Reference work entry
  • First Online:

  • 99 Accesses

Synonyms

VSM

Definition

In the Vector-Space Model (VSM) for Information Retrieval (IR), every informative object (e.g., document, query, fragment, cluster, collection) can be described as a vector of a vector space defined over the real field. In most applications, the VSM for IR represents documents and queries as vectors of weights (i.e., coordinates of a vector space). Each weight is a measure of the importance of an index term in a document or a query. The index term weights are computed on the basis of the frequency of the index terms in the document, the query, or the collection. At retrieval time, the documents are ranked by a function of the inner product between the document vectors and the query vector; for example, the retrieval function can be the cosine of the angle between a document vector and a query vector. If x is the vector of the n-dimensional real field which represents an informative object to be ranked against another informative object, which is represented by...

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   6,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Recommended Reading

  1. Deerwester S, Dumais S, Furnas G, Landauer T, Harshman R. Indexing by latent semantic analysis. J Am Soc Inform Sci. 1990;41(6):391–407.

    Article  Google Scholar 

  2. Dubin D. The most influential paper Gerard Salton never wrote. Libr Trends. 2004;52(4):748–64.

    Google Scholar 

  3. Halmos P. Finite-dimensional vector spaces. Undergraduate texts in mathematics. New York: Springer; 1987.

    Google Scholar 

  4. Melucci M. Context modeling and discovery using vector space bases. In: Proceedings of the Annual ACM Conference on Information and Knowledge Management; 2005. p. 808–15.

    Google Scholar 

  5. Melucci M. A basis for information retrieval in context. ACM Trans Inform Syst. 2008;26(3):1–41.

    Article  Google Scholar 

  6. Melucci M. Introduction to information retrieval and quantum mechanics. Berlin: Springer; 2015.

    Book  MATH  Google Scholar 

  7. Paik JH. A novel TF-IDF weighting scheme for effective ranking. In: Proceedings of the 36th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 2013. p. 343–52.

    Google Scholar 

  8. Salton G. Associative document retrieval techniques using bibliographic information. J ACM. 1963;10(4):440–57.

    Article  MATH  Google Scholar 

  9. Salton G. Mathematics and information retrieval. J Doc. 1979;35(1):1–29.

    Article  Google Scholar 

  10. Salton G. Automatic text processing. Reading: Addison-Wesley; 1989.

    Google Scholar 

  11. Salton G, Buckley C. Term weighting approaches in automatic text retrieval. Inform Process Manage. 1988;24(5):513–23.

    Article  Google Scholar 

  12. Salton G, Wong A, Yang C. A vector space model for automatic indexing. Commun ACM. 1975;18(11):613–20

    Article  MATH  Google Scholar 

  13. Salton G, Yang C, Yu C. A theory of term importance in automatic text analysis. J Am Soc Inform Sci. 1975;26(1):33–44.

    Article  Google Scholar 

  14. Salton G, Allan J, Buckley C. Approaches to passage retrieval in full text information systems. In: Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 1993. p. 49–58.

    Google Scholar 

  15. Salton G, Singhal A, Buckley C, Mitra M. Automatic text decomposition using text segments and text themes. In: Proceedings of the ACM Hypertext Conference; 1996. p. 53–65.

    Google Scholar 

  16. Salton G, Singhal A, Mitra M, Buckley C. Automatic text structuring and summarization. Inf Proc Manag. 1997;33(2):193–207.

    Article  Google Scholar 

  17. Singhal A, Buckley C, Mitra M. Pivoted document length normalization. In: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 1996. p. 21–29.

    Google Scholar 

  18. van Rijsbergen C. The geometry of information retrieval. Cambridge: Cambridge University Press; 2004.

    Book  MATH  Google Scholar 

  19. Wong S, Raghavan V. Vector space model of information retrieval – a reevaluation. In: Proceedings of the 7th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 1984. p. 167–85.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Padua, Padua, Italy

    Massimo Melucci

Authors
  1. Massimo Melucci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Massimo Melucci .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Melucci, M. (2018). Vector-Space Model. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_918

Download citation

Publish with us

Policies and ethics

Search

Navigation