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Vector space model or term vector model is an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers, such as, for example, index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings. Its first use was in the SMART Information Retrieval System.



Documents and queries are represented as vectors.


Each dimension corresponds to a separate term. If a term occurs in the document, its value in the vector is non-zero. Several different ways of computing these values, also known as (term) weights, have been developed. One of the best known schemes is tf-idf weighting (see the example below).

The definition of term depends on the application. Typically terms are single words, keywords, or longer phrases. If words are chosen to be the terms, the dimensionality of the vector is the number of words in the vocabulary (the number of distinct words occurring in the corpus).

Vector operations can be used to compare documents with queries.


Relevance rankings of documents in a keyword search can be calculated, using the assumptions of document similarities theory, by comparing the deviation of angles between each document vector and the original query vector where the query is represented as the same kind of vector as the documents.

In practice, it is easier to calculate the cosine of the angle between the vectors, instead of the angle itself:


Where   is the intersection (i.e. the dot product) of the document (d2 in the figure to the right) and the query (q in the figure) vectors,   is the norm of vector d2, and   is the norm of vector q. The norm of a vector is calculated as such:


As all vectors under consideration by this model are elementwise nonnegative, a cosine value of zero means that the query and document vector are orthogonal and have no match (i.e. the query term does not exist in the document being considered). See cosine similarity for further information.

Example: tf-idf weightsEdit

In the classic vector space model proposed by Salton, Wong and Yang [1] the term-specific weights in the document vectors are products of local and global parameters. The model is known as term frequency-inverse document frequency model. The weight vector for document d is  , where



  •   is term frequency of term t in document d (a local parameter)
  •   is inverse document frequency (a global parameter).   is the total number of documents in the document set;   is the number of documents containing the term t.

Using the cosine the similarity between document dj and query q can be calculated as:



The vector space model has the following advantages over the Standard Boolean model:

  1. Simple model based on linear algebra
  2. Term weights not binary
  3. Allows computing a continuous degree of similarity between queries and documents
  4. Allows ranking documents according to their possible relevance
  5. Allows partial matching

Most of these advantages are a consequence of the difference in the density of the document collection representation between Boolean and tf-idf approaches. When using Boolean weights, any document lies in a vertex in a n-dimensional hypercube. Therefore, the possible document representations are   and the maximum Euclidean distance between pairs is  . As documents are added to the document collection, the region defined by the hypercube's vertexes become more populated and hence denser. Unlike Boolean, when a document is added using tf-idf weights, the idfs of the terms in the new document decrease while that of the remaining terms increase. In average, as documents are added, the region where documents lie expands regulating the density of the entire collection representation. This behavior models the original motivation of Salton and his colleagues that a document collection represented in a low density region could yield better retrieval results.


The vector space model has the following limitations:

  1. Long documents are poorly represented because they have poor similarity values (a small scalar product and a large dimensionality)
  2. Search keywords must precisely match document terms; word substrings might result in a "false positive match"
  3. Semantic sensitivity; documents with similar context but different term vocabulary won't be associated, resulting in a "false negative match".
  4. The order in which the terms appear in the document is lost in the vector space representation.
  5. Theoretically assumed terms are statistically independent.
  6. Weighting is intuitive but not very formal.

Many of these difficulties can, however, be overcome by the integration of various tools, including mathematical techniques such as singular value decomposition and lexical databases such as WordNet.

Models based on and extending the vector space modelEdit

Software that implements the vector space modelEdit

The following software packages may be of interest to those wishing to experiment with vector models and implement search services based upon them.

Free open source softwareEdit

Further readingEdit

See alsoEdit


  1. ^ G. Salton , A. Wong , C. S. Yang, A vector space model for automatic indexing, Communications of the ACM, v.18 n.11, p.613-620, Nov. 1975