Document Similarity
Document Similarity is a measure in vector space retrieval that quantifies the degree of resemblance between two documents based on their vector representations in a high-dimensional space, typically computed using cosine similarity or related metrics.
1.4K
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Publications per year
2017–2026
302
Parents
1.4K
Indexing by latent semantic analysis
Scott Deerwester, Susan Dumais, George W. Furnas et al. · Journal of the American Society for Information Science · 1990 · 12.8K citations
A vector space model for automatic indexing
Gerard Salton, Anita M.-Y. Wong, Chul‐Su Yang · Communications of the ACM · 1975 · 7.4K citations · Full text
On the resemblance and containment of documents
Arndt Bröder · 2002 · 1.7K citations
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Peking University
Beijing, China
17 h-index
46 publications
1.5K citations
University of Maryland, College Park
College Park, United States
16 h-index
38 publications
3.2K citations
Microsoft (United States)
Redmond, United States
16 h-index
34 publications
2.5K citations
Stanford University
Stanford, United States
16 h-index
31 publications
6.5K citations

City University of Hong Kong
Hong Kong, Hong Kong
12 h-index
30 publications
1.3K citations
Tsinghua University
Beijing, China
12 h-index
29 publications
1.4K citations

Cornell University
Ithaca, United States
12 h-index
26 publications
29.7K citations

University of Massachusetts Amherst
Amherst Center, United States
11 h-index
25 publications
1.2K citations
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25 publications · 1.6K citations
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JASIS · 32 publications · 14.3K citations
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TKDE · 21 publications · 1.7K citations
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16 publications · 1.1K citations
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Repository · 30 publications · 902 citations
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