DALI：LLM-Agent增强的双流自适应领导力识别用于群体推荐
DALI: LLM-Agent Enhanced Dual-Stream Adaptive Leadership Identification for Group Recommendations
AI总结：
DALI通过结合大语言模型的符号推理能力与神经网络表示学习，解决群体推荐中区分领导主导与协作群体的问题，提升推荐准确性。
摘要：
群体推荐系统在支持各种情境下的集体决策中起关键作用，从休闲活动到组织团队建设。现有方法通常使用手工编写的聚合规则或神经聚合模型，但两者在区分领导主导与协作群体和准确表示真实群体偏好方面存在不足，尤其是在单一成员 disproportionately 影响群体选择时。为解决这些限制，我们提出了双流自适应领导力识别（DALI）框架，该框架独特地结合了大语言模型（LLMs）的符号推理能力与基于神经网络的表示学习。具体而言，DALI引入了两个关键创新：一个动态规则生成模块，通过迭代性能反馈自主制定和演化识别规则；以及一个神经符号聚合机制，同时利用符号推理来稳健地识别领导群体，并利用基于注意力的神经聚合来准确建模协作群体动态。在马蜂窝旅行数据集上的实验表明，与现有框架相比，DALI显著提高了推荐准确性，突显了其在复杂、真实世界群体决策环境中的动态适应能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
