Knowledge Graph-based Recommendation

Knowledge Graph-Based Recommendation is a recommendation approach that leverages structured knowledge from a knowledge graph as its primary source of side information to enhance recommendation accuracy, particularly in cold-start scenarios, by exploiting semantic relationships between entities.

457

Publications

27.7K

Citations

1.7K

Authors

520

Institutions

Publications per year

2017–2026

439

Variants

Knowledge Graph-enhanced Recommendation

Authors

1.7K

  1. XX
    Xing Xie

    Microsoft Research Asia (China), China

    11 publications · 3K citations

    11

    h-index

  2. XW
    Xiang Wang

    National University of Singapore, Singapore

    8 publications · 4.2K citations

    8

    h-index

  3. XH
    Xiangnan He

    University of Science and Technology of China, China

    7 publications · 4.2K citations

    7

    h-index

  4. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    7 publications · 4.2K citations

    7

    h-index

  5. CH
    Chao Huang

    University of Hong Kong, Hong Kong SAR China

    7 publications · 1.2K citations

    7

    h-index

1–5 of 1.7K

Venues

  1. Expert Systems with Applications

    29 publications · 1K citations

    18

    h-index

  2. Knowledge-Based Systems

    23 publications · 1K citations

    16

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 15 publications · 1.5K citations

    12

    h-index

  4. arXiv (Cornell University)

    Repository · 16 publications · 623 citations

    12

    h-index

  5. ACM Transactions on Information Systems

    11 publications · 465 citations

    11

    h-index

1–5