Ranking From Implicit Feedback is a recommendation methodology that distinguishes itself by inferring user preferences from indirect behavioral signals, such as clicks or views, to generate personalized item rankings without relying on explicit rating data.
68
Publications
10.6K
Citations
216
Authors
101
Institutions
Publications per year
2017–2026
39
Parents
Publications
68
BPR: Bayesian Personalized Ranking from Implicit Feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner et al. · arXiv (Cornell University) · 2012 · 4.4K citations · Full text
Accurately interpreting clickthrough data as implicit feedback
Thorsten Joachims, Laura Granka, Bing Pan et al. · 2005 · 1.4K citations
Unbiased Learning-to-Rank with Biased Feedback
Thorsten Joachims, Adith Swaminathan, Tobias Schnabel · 2017 · 514 citations · Full text
1–5 of 68
Authors
216
- TJ
8
h-index
- TS
3
h-index
- ZS
3
h-index
- RA
2
h-index
- HQ
2
h-index
1–5 of 216
Institutions
101
Cornell University
Ithaca, United States
8 h-index
23 publications
8.8K citations
University of Science and Technology of China
Hefei, China
4 h-index
14 publications
620 citations
Nanyang Technological University
Singapore, Singapore
4 h-index
12 publications
478 citations
Hefei University of Technology
Hefei, China
3 h-index
8 publications
186 citations
Beijing Jiaotong University
Beijing, China
3 h-index
7 publications
359 citations
Stanford University
Stanford, United States
2 h-index
2 publications
1.6K citations
Telenor (Norway)
Sandvika, Norway
2 h-index
2 publications
109 citations
University of Hildesheim
Hildesheim, Germany
2 h-index
8 publications
18K citations
1–8 of 101
Venues
- Information Sciences
4 publications · 381 citations
4
h-index
- arXiv (Cornell University)
Repository · 3 publications · 4.6K citations
3
h-index
- IEEE Transactions on Knowledge and Data Engineering
TKDE · 3 publications · 67 citations
3
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- Decision Support Systems
2 publications · 89 citations
2
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- Knowledge-Based Systems
2 publications · 197 citations
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1–5