Multi-objective Personalization

Multi-Objective Personalization is a personalization framework that simultaneously optimizes multiple, potentially conflicting user or system objectives—such as relevance, diversity, fairness, and privacy—within web-based adaptive systems, distinguishing itself from single-objective approaches by integrating trade-off-aware decision-making mechanisms.

14

Publications

420

Citations

56

Authors

23

Institutions

Publications per year

2017–2026

12

Authors

56

  1. RA
    Rawan Alkurd

    Carleton University, Canada

    2 publications · 57 citations

    2

    h-index

  2. HY
    Halim Yanıkömeroğlu

    Carleton University, Canada

    2 publications · 57 citations

    2

    h-index

  3. IA
    Ibrahim Abualhaol

    Carleton University, Canada

    2 publications · 57 citations

    2

    h-index

  4. QX
    Qingzheng Xu

    Xi'an University of Technology, China

    1 publications · 47 citations

    1

    h-index

  5. SC
    Saliha Chellak

    Cadi Ayyad University, Morocco

    1 publications · 15 citations

    1

    h-index

1–5 of 56

Venues

  1. IEEE Access

    2 publications · 43 citations

    2

    h-index

  2. Neurocomputing

    1 publications · 47 citations

    1

    h-index

  3. 1

    h-index

  4. Mathematics

    1 publications · 15 citations

    1

    h-index

  5. Complexity

    1 publications · 36 citations

    1

    h-index

1–5