Multi-attribute Recommendation
Multi-Attribute Recommendation is a recommendation paradigm that, as a genus, generates personalized suggestions by leveraging auxiliary item or user attributes beyond explicit ratings or binary interactions, and, as a differentia, explicitly integrates multiple heterogeneous attribute types—such as demographic, contextual, or content-based features—into the modeling process to enhance recommendation accuracy and explainability.
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174
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2017–2026
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It takes variety to make a world
Cong Yu, Laks V. S. Lakshmanan, Sihem Amer-Yahia · 2009 · 167 citations · Full text
Joint Item Recommendation and Attribute Inference
Le Wu, Yonghui Yang, Kun Zhang et al. · 2020 · 102 citations
Computing, Data Science, Data Mining +12
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Hefei University of Technology
Hefei, China
4 h-index
11 publications
680 citations

City University of Hong Kong
Hong Kong, Hong Kong
3 h-index
9 publications
229 citations

Anhui University
Hefei, China
3 h-index
13 publications
436 citations

Agricultural University of Athens
Athens, Greece
2 h-index
4 publications
550 citations

Sun Yat-sen University
Guangzhou, China
2 h-index
9 publications
213 citations
Shandong University of Science and Technology
Qingdao, China
2 h-index
4 publications
93 citations
National University of Singapore
Singapore, Singapore
2 h-index
7 publications
127 citations
Hong Kong Baptist University
Hong Kong, Hong Kong
2 h-index
4 publications
282 citations
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4 publications · 294 citations
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4 publications · 98 citations
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4 publications · 176 citations
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2 publications · 61 citations
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2 publications · 67 citations
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