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

Authors

1.9K

  1. DJ
    Dietmar Jannach

    University of Klagenfurt, Austria

    6 publications · 406 citations

    6

    h-index

  2. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    5 publications · 723 citations

    5

    h-index

  3. RB
    Robin Burke

    DePaul University, United States

    5 publications · 215 citations

    5

    h-index

  4. FF
    Fuli Feng

    University of Science and Technology of China, China

    5 publications · 584 citations

    5

    h-index

  5. BS
    Barry Smyth

    University College Dublin, Ireland

    5 publications · 590 citations

    5

    h-index

1–5 of 1.9K

Venues

  1. Expert Systems with Applications

    28 publications · 1.6K citations

    21

    h-index

  2. Knowledge-Based Systems

    17 publications · 941 citations

    17

    h-index

  3. Information Sciences

    13 publications · 549 citations

    13

    h-index

  4. Applied Soft Computing

    12 publications · 401 citations

    11

    h-index

  5. IEEE Access

    11 publications · 284 citations

    11

    h-index

1–5