Hashtag Recommendation

Hashtag Recommendation is a computational method that identifies and suggests relevant hashtags for user-generated content by analyzing contextual, semantic, and usage patterns within social media data.

88

Publications

3.7K

Citations

252

Authors

105

Institutions

Publications per year

2017–2026

48

Authors

252

  1. YG
    Yeyun Gong

    Microsoft Research Asia (China), China

    6 publications · 268 citations

    6

    h-index

  2. QZ
    Qi Zhang

    Fudan University, China

    5 publications · 302 citations

    5

    h-index

  3. YL
    Yang Li

    Northeast Forestry University, China

    4 publications · 136 citations

    4

    h-index

  4. XH
    Xuanjing Huang

    Fudan University, China

    4 publications · 217 citations

    4

    h-index

  5. NK
    Nagendra Kumar

    Indian Institute of Technology Indore, India

    4 publications · 134 citations

    4

    h-index

1–5 of 252

Venues

  1. 4

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 56 citations

    3

    h-index

  3. Expert Systems with Applications

    3 publications · 78 citations

    3

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 60 citations

    2

    h-index

  5. Knowledge-Based Systems

    2 publications · 53 citations

    2

    h-index

1–5