Cold-start Session Recommendation

Cold-Start Session Recommendation is a session-based recommendation approach that specifically addresses the challenge of making accurate recommendations in sessions where no prior interaction history is available, thereby distinguishing itself from general session-based methods by focusing on initial-user or first-time interaction scenarios.

11

Publications

336

Citations

48

Authors

29

Institutions

Publications per year

2017–2026

7

Authors

48

  1. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    2 publications · 70 citations

    2

    h-index

  2. SO
    Sejoon Oh

    Seoul National University, South Korea

    1 publications · 22 citations

    1

    h-index

  3. WZ
    Wanli Zuo

    Jilin University, China

    1 publications · 46 citations

    1

    h-index

  4. CC
    Chien Chin Chen

    National Taiwan University, Taiwan

    1 publications · 23 citations

    1

    h-index

  5. HL
    Hanqing Lu

    Chinese Academy of Sciences, China

    1 publications · 23 citations

    1

    h-index

1–5 of 48

Venues

  1. Neurocomputing

    2 publications · 71 citations

    2

    h-index

  2. World Wide Web

    1 publications · 15 citations

    1

    h-index

  3. Applied Intelligence

    1 publications · 23 citations

    1

    h-index

  4. Information Sciences

    1 publications · 41 citations

    1

    h-index

  5. Neural Information Processing Systems

    Conference · 1 publications · 38 citations

    1

    h-index

1–5