Vector space models

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A vector space model is an algebraic model that represents both documents and queries as vectors in a multidimensional space. Here's how it works:

1. A term-document matrix is ​​created , where the rows are terms and the columns are documents.

2. A query vector is formed based on the user's search terms

3. The system calculates a numerical score using a measure called cosine similarity, which hong kong whatsapp number data determines the degree of coincidence between the query vector and the document vectors.

Information retrieval: Vector space model

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As an information retrieval system, documents are ranked based on these scores, with the highest ranked being the most relevant.

advantages

Retrieves items even if only some terms match
Variations in term usage and document length, allowing for accommodation of different document types
Cons

Larger vocabularies and document collections make similarity calculations resource intensive.