Incremental User Profiling

Incremental User Profiling is a dynamic user modeling technique in content-based recommendation systems that continuously refines a user's preference profile by integrating new interaction data in real time, distinguishing itself from batch or static profiling methods through its ability to adapt incrementally without reprocessing historical data.

11

Publications

709

Citations

30

Authors

16

Institutions

Publications per year

2017–2026

2

Authors

30

  1. YK
    Yehuda Koren

    AT&T (United States), United States

    2 publications · 255 citations

    2

    h-index

  2. RL
    Ronny Lempel

    Technion – Israel Institute of Technology, Israel

    2 publications · 255 citations

    2

    h-index

  3. NG
    Nadav Golbandi

    Google (United States), United States

    2 publications · 255 citations

    2

    h-index

  4. CL
    C. Lee Giles

    Pennsylvania State University, United States

    1 publications · 83 citations

    1

    h-index

  5. YF
    Yanjie Fu

    University of Central Florida, United States

    1 publications · 72 citations

    1

    h-index

1–5 of 30

Venues

  1. Information Processing & Management

    1 publications · 46 citations

    1

    h-index

  2. 1

    h-index

  3. IEEE Transactions on Consumer Electronics

    1 publications · 91 citations

    1

    h-index

  4. American Journal of Applied Sciences

    AJAS · 1 publications · 25 citations

    1

    h-index

  5. Machine Learning

    1 publications · 1.2K citations

1–5