通过建模组员互动关系，提升小群体推荐满意度。
Leveraging the Dynamics of Leadership in Group Recommendation Systems
- 用图结构表示组员间信任与互动，动态捕捉关系变化
- 在真实餐厅推荐场景中，显著提升组员整体满意度
- 适合关注群体协作与社交影响力的推荐系统研究者
在群体推荐系统（GRS）领域，有效应对成员间多样化偏好是一大挑战。传统方法通常将个体偏好聚合为集体偏好生成推荐，可能忽略成员间的复杂互动。本文提出一种新方法，聚焦于有共同兴趣的小群体。具体而言，设计了一款基于网页的餐厅推荐系统，通过建模组员间的相互作用来提升用户满意度。受群体决策文献和图论启发，提出一种推荐算法，强调组内关系与信任的动态性。将组员表示为节点，互动关系表示为有向边，算法捕捉成对关系以促进共识，改善推荐与群体偏好的一致性。该交互导向框架旨在提升推荐结果的整体群体满意度。
原文摘要 · Abstract (English)
In the field of group recommendation systems (GRS), effectively addressing the diverse preferences of group members poses a significant challenge. Traditional GRS approaches often aggregate individual preferences into a collective group preference to generate recommendations, which may overlook the intricate interactions between group members. We introduce a novel approach to group recommendation, with a specific focus on small groups sharing common interests. In particular, we present a web-based restaurant recommendation system that enhances user satisfaction by modeling mutual interactions among group members. Drawing inspiration from group decision-making literature and leveraging graph theory, we propose a recommendation algorithm that emphasizes the dynamics of relationships and trust within the group. By representing group members as nodes and their interactions as directed edges, the algorithm captures pairwise relationships to foster consensus and improve the alignment of recommendations with group preferences. This interaction-focused framework ultimately seeks to enhance overall group satisfaction with the recommended choices.
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