用大模型生成最优活动日程，兼顾效率与可解释性。
CoS: Towards Optimal Event Scheduling via Chain-of-Scheduling
- 将日程规划拆解为探索、验证、整合三阶段，引导大模型生成
- 在三个真实数据集上接近理论最优效果，且推理高效
- 零样本迁移能力强，适合跨领域活动推荐场景
在基于事件的社交网络中，推荐活动日程是维持用户活跃的关键。有效推荐需在时间与地理约束下最大化用户偏好。现有方法因问题本身的NP难性质，在效率、效果与泛化能力间存在固有权衡。本文提出链式日程框架（CoS），通过受控的高效流程激活大语言模型（LLM）的日程规划能力。CoS将任务分解为探索、验证与整合三个基础阶段，并借助知识蒸馏（KD）使LLM自主生成完整日程。实验表明，CoS在三个真实数据集上实现接近理论最优的效果，兼具高效率和可解释性，且在域外数据上表现出强大的零样本学习能力。
原文摘要 · Abstract (English)
Recommending event schedules is a key issue in Event-based Social Networks (EBSNs) in order to maintain user activity. An effective recommendation is required to maximize the user's preference, subjecting to both time and geographical constraints. Existing methods face an inherent trade-off among efficiency, effectiveness, and generalization, due to the NP-hard nature of the problem. This paper proposes the Chain-of-Scheduling (CoS) framework, which activates the event scheduling capability of Large Language Models (LLMs) through a guided, efficient scheduling process. CoS enhances LLM by formulating the schedule task into three atomic stages, i.e., exploration, verification and integration. Then we enable the LLMs to generate CoS autonomously via Knowledge Distillation (KD). Experimental results show that CoS achieves near-theoretical optimal effectiveness with high efficiency on three real-world datasets in a interpretable manner. Moreover, it demonstrates strong zero-shot learning ability on out-of-domain data.
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