Session Representation Learning

Session Representation Learning is a computational framework within session-based recommendation that models user behavior sequences as variable-length sessions and captures their latent structural patterns to generate concise, fixed-dimensional vector representations distinguishing it from broader user modeling approaches by focusing solely on anonymous, short-term interaction sequences without relying on persistent user identities.

160

Publications

12.1K

Citations

559

Authors

202

Institutions

Publications per year

2017–2026

154

Authors

559

  1. HC
    Honghui Chen

    National University of Defense Technology, China

    6 publications · 308 citations

    6

    h-index

  2. HY
    Hongzhi Yin

    The University of Queensland, Australia

    5 publications · 933 citations

    5

    h-index

  3. ZC
    Zhumin Chen

    Shandong University, China

    4 publications · 1.8K citations

    4

    h-index

  4. MD
    Maarten de Rijke

    University of Amsterdam, Netherlands

    4 publications · 528 citations

    4

    h-index

  5. ZP
    Zhiqiang Pan

    National University of Defense Technology, China

    4 publications · 317 citations

    4

    h-index

1–5 of 559

Venues

  1. Neurocomputing

    12 publications · 345 citations

    12

    h-index

  2. Knowledge-Based Systems

    10 publications · 292 citations

    10

    h-index

  3. arXiv (Cornell University)

    Repository · 8 publications · 199 citations

    8

    h-index

  4. Expert Systems with Applications

    7 publications · 219 citations

    7

    h-index

  5. IEEE Access

    7 publications · 123 citations

    7

    h-index

1–5