<p>Information about YM sub-datasets.</p>

The rapid advancement of technology has enabled the collection of detailed, multi-dimensional user data, paving the way for multi-criteria recommendation systems that consider diverse aspects of user preferences. While traditional recommendation systems aim to satisfy individual users, group recommendation systems are designed to generate suggestions that accommodate the collective preferences of a group. However, the increasing prevalence of group interactions in digital environments has also introduced new vulnerabilities, such as group shilling attacks, where coordinated malicious users manipulate recommendation outcomes. This study conducts the first comprehensive robustness analysis of multi-criteria group recommender systems, addressing a critical research gap. A novel shilling attack strategy is proposed by adapting the group shilling model to multi-criteria settings, allowing a deeper understanding of the risks these systems face. Experimental results indicate that the proposed multi-criteria recommender system achieves notable robustness across datasets. Specifically, the average hit ratio (AvgHR) increases up to approximately 12% on the YM20 dataset and reaches around 15% on the YM10 dataset. Furthermore, among the target item selection strategies, the MUP-NNZ method consistently demonstrates superior resistance to profile injection attacks, confirming its effectiveness in maintaining recommendation accuracy under adversarial conditions.技术的飞速进步推动了精细化多维度用户数据的采集，为兼顾用户偏好多维度特征的多准则推荐系统（multi-criteria recommendation system）的发展铺平了道路。传统推荐系统旨在满足单个用户的个性化需求，而群组推荐系统则致力于生成契合群组集体偏好的推荐方案。然而，数字环境中群组交互的日益普及也催生了全新的安全隐患，例如群组注水攻击（group shilling attack）——即由协同恶意用户操纵推荐结果的恶意攻击手段。本研究首次针对多准则群组推荐系统开展全面的鲁棒性分析，填补了该领域的关键研究空白。本研究将群组注水攻击模型适配至多准则场景，提出了一种全新的注水攻击策略，从而能够更深入地剖析此类系统所面临的潜在风险。实验结果表明，所提出的多准则推荐系统在各类数据集上均展现出显著的鲁棒性。具体而言，在YM20数据集上，平均命中率（average hit ratio, AvgHR）最高提升约12%；而在YM10数据集上，该指标可达约15%。此外，在目标项目选择策略中，MUP-NNZ方法始终展现出更优异的资料注入攻击抵御能力，证实了其在对抗性条件下维持推荐准确率的有效性。