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

Publications

5.7K

Citations

106

Authors

52

Institutions

Publications per year

2017–2026

16

Authors

106

  1. TM
    Taghi M. Khoshgoftaar

    Florida Atlantic University, United States

    2 publications · 3.6K citations

    2

    h-index

  2. XS
    Xiaoyuan Su

    Florida Atlantic University, United States

    2 publications · 3.6K citations

    2

    h-index

  3. JL
    Jing Lv

    Chongqing University, China

    1 publications · 52 citations

    1

    h-index

  4. YZ
    Yuan Zhang

    Southwest University, China

    1 publications · 40 citations

    1

    h-index

  5. GK
    George Karypis

    University of Minnesota, United States

    1 publications · 106 citations

    1

    h-index

1–5 of 106

Venues

  1. IEEE Access

    2 publications · 329 citations

    2

    h-index

  2. IEEE Transactions on Industrial Informatics

    2 publications · 81 citations

    2

    h-index

  3. Scientometrics

    1 publications · 16 citations

    1

    h-index

  4. Information Sciences

    1 publications · 32 citations

    1

    h-index

  5. CAAI Transactions on Intelligence Technology

    1 publications · 46 citations

    1

    h-index

1–5