Hierarchical Session Modeling

Hierarchical Session Modeling is a session-based recommendation framework that structures user interactions into multiple levels of abstraction to capture both short-term and long-term patterns within and across sessions, distinguishing itself from flat or non-hierarchical approaches by explicitly modeling the temporal and structural dependencies in a layered manner.

8

Publications

173

Citations

27

Authors

14

Institutions

Publications per year

2017–2026

8

Authors

27

  1. CQ
    Chen Qu

    Emory University, United States

    1 publications · 19 citations

    1

    h-index

  2. CG
    Caili Guo

    Beijing University of Posts and Telecommunications, China

    1 publications · 27 citations

    1

    h-index

  3. DJ
    Dietmar Jannach

    University of Klagenfurt, Austria

    1 publications · 41 citations

    1

    h-index

  4. WX
    Wu-Dong Xi

    Sun Yat-sen University, China

    1 publications · 13 citations

    1

    h-index

  5. CF
    Chunyan Feng

    Beijing University of Posts and Telecommunications, China

    1 publications · 27 citations

    1

    h-index

1–5 of 27

Venues

  1. ACM Transactions on Information Systems

    2 publications · 38 citations

    2

    h-index

  2. User Modeling and User-Adapted Interaction

    1 publications · 41 citations

    1

    h-index

  3. Neurocomputing

    1 publications · 27 citations

    1

    h-index

  4. Knowledge-Based Systems

    1 publications · 21 citations

    1

    h-index

  5. 2022 IEEE International Conference on Big Data (Big Data)

    Conference · 1 publications · 14 citations

1–5