Recommendation systems must optimize multiple objectives while satisfying hard business constraints such as fairness and coverage. For example, an e-commerce platform may require every recommendation list to include items from multiple sellers and at least one newly listed product; violating such constraints--even once--is unacceptable in production. Prior work on multi-objective recommendation and recent LLM-based recommender agents largely treat constraints as soft penalties or focus on item scoring and interaction, leading to frequent violations in real-world deployments. How to leverage LLMs for coordinating constrained optimization in recommendation systems remains underexplored. We propose DualAgent-Rec, an LLM-coordinated dual-agent framework for constrained multi-objective e-commerce recommendation. The framework separates optimization into an Exploitation Agent that prioritizes accuracy under hard constraints and an Exploration Agent that promotes diversity through unconstrained Pareto search. An LLM-based coordinator adaptively allocates resources between agents based on optimization progress and constraint satisfaction, while an adaptive epsilon-relaxation mechanism guarantees feasibility of final solutions. Experiments on the Amazon Reviews 2023 dataset demonstrate that DualAgent-Rec achieves 100% constraint satisfaction and improves Pareto hypervolume by 4-6% over strong baselines, while maintaining competitive accuracy-diversity trade-offs. These results indicate that LLMs can act as effective orchestration agents for deployable and constraint-compliant recommendation systems.

翻译：推荐系统必须在满足公平性和覆盖率等硬性业务约束的同时优化多个目标。例如，电商平台可能要求每个推荐列表必须包含来自多个卖家的商品，且至少包含一件新上架产品；在生产环境中，违反此类约束——即使仅一次——也是不可接受的。以往关于多目标推荐的研究以及近期基于LLM的推荐智能体大多将约束视为软惩罚项，或仅关注商品评分与交互，导致实际部署中频繁出现约束违反。如何利用LLMs协调推荐系统中的约束优化问题仍未得到充分探索。我们提出DualAgent-Rec，这是一个由LLM协调的双智能体框架，用于解决约束多目标电商推荐问题。该框架将优化过程分解为：在硬约束下优先保证准确性的"利用智能体"，以及通过无约束帕累托搜索提升多样性的"探索智能体"。基于LLM的协调器根据优化进程和约束满足情况自适应分配两个智能体的资源，同时自适应ε松弛机制保证最终解的可行性。在Amazon Reviews 2023数据集上的实验表明，DualAgent-Rec实现了100%的约束满足率，帕累托超体积较基线方法提升4-6%，同时保持了具有竞争力的准确性-多样性权衡。这些结果表明，LLMs能够作为可部署且满足约束要求的推荐系统的有效编排智能体。