Memory-augmented Recommendation
Memory-Augmented Recommendation is a recommendation framework that enhances session-based user preference prediction by integrating an explicit memory module to capture and utilize long-term or cross-session behavioral patterns, thereby distinguishing it from models relying solely on short-term sequential dynamics.
38
Publications
2.8K
Citations
162
Authors
85
Institutions
Publications per year
2017–2026
35
Parents
38
Sequential Recommendation with User Memory Networks
Xu Chen, Hongteng Xu, Yongfeng Zhang et al. · 2018 · 508 citations · Full text
A Collaborative Session-based Recommendation Approach with Parallel Memory Modules
Meirui Wang, Pengjie Ren, Lei Mei et al. · 2019 · 279 citations
1–5 of 38
162
2
h-index
1
h-index
Rutgers, The State University of New Jersey, United States
1 publications · 508 citations
1
h-index
1
h-index
1
h-index
1–5 of 162
85
Tsinghua University
Beijing, China
2 h-index
7 publications
2.1K citations
Nanyang Technological University
Singapore, Singapore
2 h-index
5 publications
479 citations

The University of Queensland
Brisbane, Australia
2 h-index
4 publications
169 citations

University of Science and Technology Beijing
Beijing, China
2 h-index
4 publications
65 citations
Earthquake Planning and Protection Organization
Athens, Greece
1 h-index
1 publications
38 citations
Institute for Complex Systems
Rome, Italy
1 h-index
1 publications
16 citations

Coventry University
Coventry, United Kingdom
1 h-index
1 publications
26 citations
Zhejiang University of Science and Technology
Hangzhou, China
1 h-index
4 publications
152 citations
1–8 of 85
3 publications · 271 citations
3
h-index
3 publications · 70 citations
3
h-index
IJGI · 2 publications · 37 citations
2
h-index
JECP · 1 publications · 17 citations
1
h-index
TOMCCAP · 1 publications · 50 citations
1
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1–5