Recommendation Explanation Generation
Recommendation Explanation Generation is the computational process of producing human-readable justifications that articulate the underlying rationale for recommended items within conversational recommendation systems, distinguished by its integration of contextual, user-specific, and item-related factors to enhance transparency and user trust.
111
Publications
8.1K
Citations
341
Authors
132
Institutions
Publications per year
2017–2026
89
111
Explainable Recommendation a survey and new perspectives
2020 · 716 citations
Jesse Vig, Shilad Sen, John Riedl · 2009 · 303 citations
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
Nan Wang, Hongning Wang, Yiling Jia et al. · 2018 · 208 citations · Full text
1–5 of 111
341
Rutgers, The State University of New Jersey, United States
12 publications · 747 citations
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6
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6
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5
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4
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1–5 of 341
132
Rutgers, The State University of New Jersey
New Brunswick, United States
9 h-index
23 publications
1.7K citations
University of Science and Technology of China
Hefei, China
7 h-index
9 publications
755 citations
Microsoft Research Asia (China)
Beijing, China
6 h-index
12 publications
1.2K citations
Hong Kong Baptist University
Hong Kong, Hong Kong
6 h-index
11 publications
936 citations
Tsinghua University
Beijing, China
5 h-index
10 publications
491 citations

Hefei University of Technology
Hefei, China
4 h-index
14 publications
678 citations
University of California, Santa Cruz
Santa Cruz, United States
4 h-index
8 publications
620 citations
University College Dublin
Dublin, Ireland
4 h-index
8 publications
594 citations
1–8 of 132
Repository · 6 publications · 129 citations
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Conference · 6 publications · 237 citations
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3 publications · 212 citations
3
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TKDE · 3 publications · 108 citations
3
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2 publications · 357 citations
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