Knowledge Graph Recommendation

Knowledge Graph Recommendation is a recommendation method that leverages structured knowledge representations, in which entities and their semantic relationships are modeled as a graph, to enhance the accuracy and explainability of learning resource suggestions by exploiting relational reasoning over interconnected educational data.

197

Publications

13.9K

Citations

759

Authors

249

Institutions

Publications per year

2017–2026

190

Authors

759

  1. XX
    Xing Xie

    Microsoft Research Asia (China), China

    7 publications · 1.3K citations

    7

    h-index

  2. XW
    Xiang Wang

    National University of Singapore, Singapore

    6 publications · 3.6K citations

    6

    h-index

  3. ZF
    Zuohui Fu

    Rutgers, The State University of New Jersey, United States

    5 publications · 384 citations

    5

    h-index

  4. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    5 publications · 3.6K citations

    5

    h-index

  5. YZ
    Yongfeng Zhang

    Rutgers, The State University of New Jersey, United States

    5 publications · 384 citations

    5

    h-index

1–5 of 759

Venues

  1. IEEE Access

    10 publications · 322 citations

    10

    h-index

  2. Expert Systems with Applications

    10 publications · 448 citations

    10

    h-index

  3. ACM Transactions on Information Systems

    8 publications · 484 citations

    8

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 9 publications · 1.2K citations

    8

    h-index

  5. arXiv (Cornell University)

    Repository · 7 publications · 174 citations

    7

    h-index

1–5