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

Authors

162

  1. MG
    Mingxin Gan

    University of Science and Technology Beijing, China

    2 publications · 37 citations

    2

    h-index

  2. LK
    Liu Ke

    Washington University in St. Louis, United States

    1 publications · 109 citations

    1

    h-index

  3. YZ
    Yongfeng Zhang

    Rutgers, The State University of New Jersey, United States

    1 publications · 508 citations

    1

    h-index

  4. DD
    Daxiang Dong

    Baidu (China), China

    1 publications · 68 citations

    1

    h-index

  5. YW
    Yining Wang

    Carnegie Mellon University, United States

    1 publications · 32 citations

    1

    h-index

1–5 of 162

Venues

  1. Expert Systems with Applications

    3 publications · 271 citations

    3

    h-index

  2. Knowledge-Based Systems

    3 publications · 70 citations

    3

    h-index

  3. ISPRS International Journal of Geo-Information

    IJGI · 2 publications · 37 citations

    2

    h-index

  4. Journal of Experimental Child Psychology

    JECP · 1 publications · 17 citations

    1

    h-index

  5. 1

    h-index

1–5