Rating Matrix Completion
Rating Matrix Completion is a predictive modeling task in recommendation systems that involves estimating missing entries in a user-item rating matrix by leveraging observed ratings and structural patterns, distinguishing itself from general prediction tasks through its focus on recovering latent user preferences and item characteristics within sparse observational data.
38
Publications
3.3K
Citations
114
Authors
56
Institutions
Publications per year
2017–2026
16
38
Graph Convolutional Matrix Completion
Rianne van den Berg, Thomas Kipf, Max Welling · arXiv (Cornell University) · 2017 · 1.1K citations · Full text
Fast online SVD revisions for lightweight recommender systems
Matthew Brand · 2003 · 183 citations · Full text
Hybrid Recommender System based on Autoencoders
2016 · 166 citations · Full text
1–5 of 38
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Hong Kong University of Science and Technology, Hong Kong SAR China
1 publications · 242 citations
1
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1–5 of 114
56
Michigan State University
East Lansing, United States
3 h-index
13 publications
883 citations
Yonsei University
Seoul, South Korea
2 h-index
3 publications
126 citations
Tsinghua University
Beijing, China
2 h-index
6 publications
152 citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
1 h-index
1 publications
19 citations

Centre National de la Recherche Scientifique
Paris, France
1 h-index
3 publications
498 citations
University of Cambridge
Cambridge, United Kingdom
1 h-index
1 publications
26 citations
IMEC
Leuven, Belgium
1 h-index
3 publications
57 citations

Langley Research Center
Hampton, United States
1 h-index
1 publications
155 citations
1–8 of 56
Repository · 3 publications · 1.1K citations
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1 publications · 28 citations
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Conference · 1 publications · 21 citations
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1 publications · 57 citations
1
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1 publications · 21 citations
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