Multimodal Recommendation

Multimodal Recommendation is a recommendation approach that integrates and processes multiple types of input data modalities—such as text, visual, auditory, and behavioral signals—to generate personalized educational recommendations by leveraging the complementary strengths of heterogeneous information sources.

156

Publications

9.1K

Citations

549

Authors

186

Institutions

Publications per year

2017–2026

121

Authors

549

  1. LN
    Liqiang Nie

    Shandong University, China

    9 publications · 1.3K citations

    9

    h-index

  2. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    8 publications · 1.3K citations

    8

    h-index

  3. YW
    Yinwei Wei

    National University of Singapore, Singapore

    6 publications · 1.3K citations

    6

    h-index

  4. XW
    Xiang Wang

    National University of Singapore, Singapore

    5 publications · 1.2K citations

    5

    h-index

  5. FL
    Fan Liu

    National University of Singapore, Singapore

    5 publications · 314 citations

    5

    h-index

1–5 of 549

Venues

  1. IEEE Transactions on Multimedia

    12 publications · 1.1K citations

    12

    h-index

  2. Expert Systems with Applications

    5 publications · 218 citations

    5

    h-index

  3. arXiv (Cornell University)

    Repository · 4 publications · 96 citations

    4

    h-index

  4. IEEE Access

    4 publications · 113 citations

    4

    h-index

  5. 4

    h-index

1–5