Multi-objective Recommendation
Multi-Objective Recommendation is a recommendation framework that, as a singular methodological approach, integrates and optimizes multiple competing objectives—such as accuracy, diversity, fairness, novelty, and coverage—by explicitly modeling trade-offs among them, thereby distinguishing it from single-object wan or sequentially addressed multi-goal recommendation strategies.
85
Publications
4.5K
Citations
419
Authors
155
Institutions
Publications per year
2017–2026
67
85
Recommending what video to watch next
Zhe Zhao, Lichan Hong, Wei Li et al. · 2019 · 309 citations
A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation
Hongjie Chen, Changhua Pei, Fei Sun et al. · 2019 · 145 citations
A survey of recommender systems with multi-objective optimization
Yong Zheng, David Wang · Neurocomputing · 2021 · 145 citations
1–5 of 85
419
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155

Xidian University
Xi'an, China
6 h-index
29 publications
2.2K citations
Taiyuan University of Science and Technology
Taiyuan, China
4 h-index
14 publications
1K citations

Chinese Academy of Sciences
Beijing, China
4 h-index
5 publications
293 citations

Shenzhen University
Shenzhen, China
3 h-index
14 publications
758 citations

The University of Queensland
Brisbane, Australia
3 h-index
7 publications
236 citations
Universidade Federal de Minas Gerais
Belo Horizonte, Brazil
3 h-index
12 publications
1.2K citations

Hebei University of Technology
Tianjin, China
3 h-index
14 publications
927 citations
University of Pittsburgh
Pittsburgh, United States
3 h-index
3 publications
131 citations
1–8 of 155
8 publications · 406 citations
8
h-index
7 publications · 507 citations
7
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4 publications · 268 citations
4
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3 publications · 205 citations
3
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3 publications · 84 citations
3
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1–5