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Vector-Space Model

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Encyclopedia of Database Systems

Synonyms

VSM

Definition

The Vector-Space Model (VSM) for Information Retrieval represents documents and queries as vectors of weights. Each weight is a measure of the importance of an index term in a document or a query, respectively. 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 the cosine of the angle between the document vectors and the query vector. For each document and query, the cosine of the angle is calculated as the ratio between the inner product between the document vector and the query vector, and the product of the norm of the document vector by the norm of the query vector. The documents are then returned by the system by decreasing cosine.

Historical Background

The use of vectors for modeling IR systems dates back to the early days of IR, especially as a tool for describing how a system should be designed and implemented. The popularity of...

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Recommended Reading

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

    Article  Google Scholar 

  2. Dubin D. The most influential paper Gerard Salton never wrote. Libr. Trends, 52(4):748–764, 2004.

    Google Scholar 

  3. Halmos P. Finite-Dimensional Vector Spaces. Undergraduate Texts in Mathematics, Springer, 1987.

    Google Scholar 

  4. Melucci M. A basis for information retrieval in context. ACM Trans. Inform. Syst., 26(3), 2008.

    Google Scholar 

  5. Salton G. Associative document retrieval techniques using bibliographic information. J. ACM, 10440–457, 1963.

    Article  MATH  Google Scholar 

  6. Salton G. Automatic Text Processing. Addison-Wesley, 1989.

    Google Scholar 

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

    Article  Google Scholar 

  8. Salton G. and Buckley C. Term Weighting Approaches in Automatic Text Retrieval. Inform. Process. Manage., 24(5):513–523, 1988.

    Article  Google Scholar 

  9. Salton G., Wong A., and Yang C. A vector space model for automatic indexing. Commun. ACM, 18(11):613–620, 1975.

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  11. Singhal A., Buckley C., and Mitra M. Pivoted Document Length Normalization. In Proc. 19th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 21–29.1996.

    Google Scholar 

  12. van Rijsbergen C. The Geometry of Information Retrieval. Cambridge University Press, UK, 2004.

    MATH  Google Scholar 

  13. Wong S. and Raghavan V. Vector space model of information retrieval – a reevaluation. In Proc. 7th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 1984, pp. 167–185.

    Google Scholar 

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Melucci, M. (2009). Vector-Space Model. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_918

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