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.

翻译：在群组推荐系统（GRS）领域，有效处理群组成员多样化的偏好是一项重大挑战。传统的GRS方法通常将个体偏好聚合为集体群组偏好以生成推荐，这可能忽略了群组成员之间复杂的交互作用。我们提出了一种新颖的群组推荐方法，特别关注具有共同兴趣的小型群组。具体而言，我们介绍了一个基于网络的餐厅推荐系统，该系统通过建模群组成员间的相互交互来提升用户满意度。借鉴群组决策文献并利用图论，我们提出了一种强调群组内关系动态与信任的推荐算法。通过将群组成员表示为节点、将其交互表示为有向边，该算法捕捉成对关系以促进共识，并提高推荐与群组偏好的匹配度。这一以交互为中心的框架最终旨在提升群组对所推荐选项的整体满意度。