Multimodal Recommendation
Multimodal Recommendation is a recommendation approach that integrates and processes multiple types of input data modalities—such as text, visual, auditory, and behavioral signals—to generate personalized educational recommendations by leveraging the complementary strengths of heterogeneous information sources.
156
Publications
9.1K
Citations
549
Authors
186
Institutions
Publications per year
2017–2026
121
156
Yinwei Wei, Xiang Wang, Liqiang Nie et al. · 2019 · 649 citations
pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis
Mining Latent Structures for Multimedia Recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu et al. · 2021 · 306 citations · Full text
Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network
Xu Chen, Hanxiong Chen, Hongteng Xu et al. · 2019 · 268 citations · Full text
Multi-modal Knowledge Graphs for Recommender Systems
Rui Sun, Xuezhi Cao, Yan Zhao et al. · 2020 · 261 citations
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186
National University of Singapore
Singapore, Singapore
15 h-index
30 publications
3.5K citations
Shandong University
Jinan, China
10 h-index
20 publications
2.1K citations
University of Science and Technology of China
Hefei, China
8 h-index
9 publications
1.4K citations

Hefei University of Technology
Hefei, China
8 h-index
18 publications
1.5K citations

Chinese Academy of Sciences
Beijing, China
8 h-index
16 publications
1.7K citations
Tsinghua University
Beijing, China
7 h-index
14 publications
1.2K citations
Nanyang Technological University
Singapore, Singapore
5 h-index
13 publications
2K citations

Zhejiang University
Hangzhou, China
5 h-index
19 publications
1.1K citations
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12 publications · 1.1K citations
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5 publications · 218 citations
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Repository · 4 publications · 96 citations
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4 publications · 113 citations
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4 publications · 133 citations
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