Group recommendation systems play a pivotal role in supporting collective decisions across various contexts, from leisure activities to organizational team-building. Existing group recommendation approaches typically use either handcrafted aggregation rules (e.g. mean, least misery, weighted sum) or neural aggregation models (e.g. attention-based deep learning frameworks), yet both fall short in distinguishing leader-dominated from collaborative groups and often misrepresent true group preferences, especially when a single member disproportionately influences group choices. To address these limitations, we propose the Dual-stream Adaptive Leadership Identification (DALI) framework, which uniquely combines the symbolic reasoning capabilities of Large Language Models (LLMs) with neural network-based representation learning. Specifically, DALI introduces two key innovations: a dynamic rule generation module that autonomously formulates and evolves identification rules through iterative performance feedback, and a neuro-symbolic aggregation mechanism that concurrently employs symbolic reasoning to robustly recognize leadership groups and attention-based neural aggregation to accurately model collaborative group dynamics. Experiments conducted on the Mafengwo travel dataset confirm that DALI significantly improves recommendation accuracy compared to existing frameworks, highlighting its capability to dynamically adapt to complex, real-world group decision environments.

翻译：群体推荐系统在支持各类场景下的集体决策中发挥着关键作用，涵盖从休闲活动到组织团队建设等领域。现有群体推荐方法通常采用手工设计的聚合规则（如均值、最小痛苦法、加权求和）或神经聚合模型（如基于注意力的深度学习框架），但两者均未能有效区分领导主导型群体与协作型群体，且常曲解真实群体偏好，尤其在单一成员对群体选择产生不成比例影响时。为克服这些局限，我们提出双流自适应领导力识别（DALI）框架，该框架独特地融合了大语言模型（LLM）的符号推理能力与基于神经网络的表示学习。具体而言，DALI引入两项关键创新：动态规则生成模块，通过迭代绩效反馈自主制定并演化识别规则；以及神经符号聚合机制，并行运用符号推理以稳健识别领导群体，同时结合基于注意力的神经聚合以精确建模协作型群体动态。在蚂蜂窝旅游数据集上的实验表明，与现有框架相比，DALI显著提升了推荐准确率，凸显其动态适应复杂现实群体决策环境的能力。