Implicit Matrix Factorization
Implicit Matrix Factorization is a matrix factorization method that models user-item interactions by treating observed user feedback as implicit indicators of preference, differentiating itself from explicit feedback approaches through its reliance on non-binary, often binary or count-based, interaction signals rather than rated evaluations.
33
Publications
2.4K
Citations
86
Authors
61
Institutions
Publications per year
2017–2026
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61

Ames Research Center
Mountain View, United States
4 h-index
4 publications
1.4K citations
University of Massachusetts Boston
Boston, United States
2 h-index
2 publications
129 citations

Peking University
Beijing, China
2 h-index
3 publications
86 citations

United States Department of the Army
Arlington, United States
1 h-index
1 publications
57 citations

Budapest University of Technology and Economics
Budapest, Hungary
1 h-index
1 publications
11 citations
Colgate University
Hamilton, United States
1 h-index
1 publications
84 citations
Toyota Technological Institute at Chicago
Chicago, United States
1 h-index
5 publications
180 citations
University of Illinois Urbana-Champaign
Urbana, United States
1 h-index
1 publications
36 citations
1–8 of 61
2 publications · 1.2K citations
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2 publications · 112 citations
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1 publications · 44 citations
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PVLDB · 1 publications · 84 citations
1
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1 publications · 16 citations
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