Temporal Dynamics

Temporal Dynamics is a modeling construct within collaborative filtering that captures the evolution of user preferences and item characteristics over time, distinguishing itself from static models by explicitly incorporating time-varying factors into the recommendation process.

4K

Publications

290.5K

Citations

15.9K

Authors

3.7K

Institutions

Publications per year

2017–2026

1.8K

Authors

15.9K

  1. MP
    Maja Pantić

    Imperial College London, United Kingdom

    13 publications · 1K citations

    11

    h-index

  2. VD
    Vince D. Calhoun

    Mind Research Network, United States

    8 publications · 414 citations

    8

    h-index

  3. JL
    Jure Leskovec

    Stanford University, United States

    8 publications · 2.1K citations

    8

    h-index

  4. LW
    Liang Wang

    Chinese Academy of Sciences, China

    7 publications · 3.5K citations

    7

    h-index

  5. MU
    M. Ugai

    Ehime University, Japan

    7 publications · 414 citations

    7

    h-index

1–5 of 15.9K

Venues

  1. NeuroImage

    56 publications · 5.1K citations

    39

    h-index

  2. Proceedings of the National Academy of Sciences

    PNAS · 44 publications · 17.3K citations

    39

    h-index

  3. PLoS ONE

    82 publications · 4K citations

    35

    h-index

  4. Scientific Reports

    76 publications · 3.2K citations

    35

    h-index

  5. arXiv (Cornell University)

    Repository · 94 publications · 5.3K citations

    32

    h-index

1–5