Multi-objective Recommendation

Multi-Objective Recommendation is a recommendation framework that, as a singular methodological approach, integrates and optimizes multiple competing objectives—such as accuracy, diversity, fairness, novelty, and coverage—by explicitly modeling trade-offs among them, thereby distinguishing it from single-object wan or sequentially addressed multi-goal recommendation strategies.

85

Publications

4.5K

Citations

419

Authors

155

Institutions

Publications per year

2017–2026

67

Authors

419

  1. MG
    Maoguo Gong

    Xidian University, China

    6 publications · 457 citations

    6

    h-index

  2. LJ
    Licheng Jiao

    Xidian University, China

    4 publications · 264 citations

    4

    h-index

  3. AL
    Anísio Lacerda

    Universidade Federal de Minas Gerais, Brazil

    4 publications · 309 citations

    4

    h-index

  4. LM
    Lijia Ma

    Xidian University, China

    3 publications · 201 citations

    3

    h-index

  5. JZ
    Jianwei Zhao

    Hebei University of Technology, China

    3 publications · 263 citations

    3

    h-index

1–5 of 419

Venues

  1. Information Sciences

    8 publications · 406 citations

    8

    h-index

  2. Expert Systems with Applications

    7 publications · 507 citations

    7

    h-index

  3. Neurocomputing

    4 publications · 268 citations

    4

    h-index

  4. Knowledge-Based Systems

    3 publications · 205 citations

    3

    h-index

  5. IEEE Access

    3 publications · 84 citations

    3

    h-index

1–5