Feature-based Recommendation
Feature-Based Recommendation is a knowledge-based recommendation approach that generates personalized suggestions by modeling item and user characteristics as explicit features, distinguishing itself through the systematic integration of feature-level attributes into the inference and decision-making process.
137
Publications
5.3K
Citations
494
Authors
199
Institutions
Publications per year
2017–2026
77
137
Content-Based Video Recommendation System Based on Stylistic Visual Features
Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi et al. · Journal on Data Semantics · 2016 · 230 citations · Full text
Learning to Rank Features for Recommendation over Multiple Categories
Xu Chen, Zheng Qin, Yongfeng Zhang et al. · 2016 · 158 citations
On-demand feature recommendations derived from mining public product descriptions
Horatiu Dumitru, Marek Gibiec, Negar Hariri et al. · 2011 · 146 citations
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199
Nanyang Technological University
Singapore, Singapore
5 h-index
9 publications
530 citations

National University of Defense Technology
Changsha, China
4 h-index
18 publications
825 citations
Tsinghua University
Beijing, China
4 h-index
8 publications
720 citations

Zhejiang University
Hangzhou, China
3 h-index
10 publications
255 citations

University of Calgary
Calgary, Canada
3 h-index
6 publications
114 citations

Alibaba Group (China)
Hangzhou, China
3 h-index
12 publications
624 citations
Hainan University
Haikou, China
3 h-index
4 publications
110 citations
Singapore Management University
Singapore, Singapore
3 h-index
5 publications
437 citations
1–8 of 199
7 publications · 251 citations
7
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5 publications · 274 citations
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4 publications · 144 citations
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Repository · 3 publications · 66 citations
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IJCIS · 2 publications · 48 citations
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