用反事实分析解释群体推荐，揭示删去某次互动后推荐如何变化。
Explaining Group Recommendations via Counterfactuals
- 基于反事实思想，通过删除历史交互预测推荐变化
- 在电影和电商数据集上验证，不同方法在效率与公平间有明显权衡
- 适合关注群体决策透明度的研究者与产品设计者
群体推荐系统帮助用户集体做决定，但缺乏透明度，使成员难以理解推荐原因。现有解释方法多针对个人，难以处理多人偏好交互。本文提出群体反事实解释框架，揭示移除特定历史互动后群体推荐的变化。我们形式化该概念，引入适用于群体的效用与公平性度量，并设计帕累托筛选、增删修剪等启发式算法以高效发现解释。在MovieLens和Amazon数据集上的实验表明：低成本方法生成更大但不公平的解释，其他方法则代价更高但结果更简洁平衡；帕累托筛选在稀疏场景下显著提升效率。
原文摘要 · Abstract (English)
Group recommender systems help users make collective choices but often lack transparency, leaving group members uncertain about why items are suggested. Existing explanation methods focus on individuals, offering limited support for groups where multiple preferences interact. In this paper, we propose a framework for group counterfactual explanations, which reveal how removing specific past interactions would change a group recommendation. We formalize this concept, introduce utility and fairness measures tailored to groups, and design heuristic algorithms, such as Pareto-based filtering and grow-and-prune strategies, for efficient explanation discovery. Experiments on MovieLens and Amazon datasets show clear trade-offs: low-cost methods produce larger, less fair explanations, while other approaches yield concise and balanced results at higher cost. Furthermore, the Pareto-filtering heuristic demonstrates significant efficiency improvements in sparse settings.
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