Memory-based Hybrid Recommendation
Memory-Based Hybrid Recommendation is a hybrid recommendation approach that integrates memory-based collaborative filtering methods with other recommendation techniques by leveraging stored user-item interaction data to compute predictions through similarity measures and neighborhood models.
30
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5.7K
Citations
106
Authors
52
Institutions
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2017–2026
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A Survey of Collaborative Filtering Techniques
Xiaoyuan Su, Taghi M. Khoshgoftaar · Advances in Artificial Intelligence · 2009 · 3.6K citations · Full text
Collaborative Filtering for Recommender Systems
Ruisheng Zhang, Qi-dong Liu, Chun-Gui et al. · 2014 · 123 citations
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Florida Atlantic University
Boca Raton, United States
2 h-index
5 publications
7.3K citations

Beihang University
Beijing, China
2 h-index
5 publications
1K citations
Jawaharlal Nehru University
New Delhi, India
1 h-index
2 publications
80 citations

University of Alberta
Edmonton, Canada
1 h-index
1 publications
19 citations
Lanzhou University
Lanzhou, China
1 h-index
5 publications
615 citations

Vietnam National University Ho Chi Minh City
Ho Chi Minh City, Vietnam
1 h-index
1 publications
32 citations

Nanjing University of Aeronautics and Astronautics
Nanjing, China
1 h-index
2 publications
62 citations
Baidu (China)
Beijing, China
1 h-index
1 publications
68 citations
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2 publications · 329 citations
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2 publications · 81 citations
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1 publications · 16 citations
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1 publications · 32 citations
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