Group Satisfaction Optimization

Group Satisfaction Optimization is a recommendation framework that specifically aims to maximize the collective satisfaction of a group by aggregating individual preferences into a unified decision, distinguishing itself from individual-level recommendation approaches through its focus on joint utility and fairness across group members.

8

Publications

225

Citations

24

Authors

13

Institutions

Publications per year

2017–2026

5

Authors

24

  1. RL
    Rui Liu

    Hong Kong Polytechnic University, Hong Kong SAR China

    2 publications · 62 citations

    2

    h-index

  2. LV
    Laks V. S. Lakshmanan

    University of British Columbia, Canada

    2 publications · 62 citations

    2

    h-index

  3. SB
    Senjuti Basu Roy

    The University of Texas at Arlington, United States

    2 publications · 62 citations

    2

    h-index

  4. RR
    René Röpke

    RWTH Aachen University, Germany

    1 publications · 13 citations

    1

    h-index

  5. XM
    Xiaolong Ma

    Southeast University, China

    1 publications · 11 citations

    1

    h-index

1–5 of 24

Venues

  1. i-com

    1 publications · 13 citations

    1

    h-index

  2. Journal of Intelligent & Fuzzy Systems

    1 publications · 11 citations

    1

    h-index

  3. Group Decision and Negotiation

    1 publications · 49 citations

    1

    h-index

  4. Multimedia Tools and Applications

    1 publications · 19 citations

    1

    h-index

  5. Expert Systems with Applications

    1 publications · 20 citations

    1

    h-index

1–5