Adaptive Recommendation
Adaptive Recommendation is a knowledge-based recommendation approach that dynamically refines its suggestions by learning from user interactions and contextual feedback over time, distinguishing itself from static or one-time recommendation methods through its capacity for continuous model updating and personalization.
635
Publications
44.2K
Citations
1.9K
Authors
666
Institutions
Publications per year
2017–2026
320
635
Analysis of recommendation algorithms for e-commerce
Badrul Sarwar, George Karypis, Joseph A. Konstan et al. · 2000 · 1.8K citations · Full text
Recommender system application developments: A survey
Jie Lü, Dianshuang Wu, Mingsong Mao et al. · Decision Support Systems · 2015 · 1.6K citations
1–5 of 635
1.9K
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1–5 of 1.9K
666
Tsinghua University
Beijing, China
14 h-index
39 publications
2.2K citations
University of Science and Technology of China
Hefei, China
14 h-index
28 publications
2.4K citations
National University of Singapore
Singapore, Singapore
12 h-index
24 publications
3.1K citations

University of Electronic Science and Technology of China
Chengdu, China
11 h-index
33 publications
1.8K citations

Zhejiang University
Hangzhou, China
11 h-index
36 publications
1.1K citations
National Cheng Kung University
Tainan City, Taiwan
10 h-index
29 publications
1.3K citations
Nanyang Technological University
Singapore, Singapore
9 h-index
21 publications
798 citations
University of Technology Sydney
Sydney, Australia
9 h-index
19 publications
9K citations
1–8 of 666
28 publications · 1.6K citations
21
h-index
17 publications · 941 citations
17
h-index
13 publications · 549 citations
13
h-index
12 publications · 401 citations
11
h-index
11 publications · 284 citations
11
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1–5