🤖 AI Summary
This work addresses the challenge of enforcing hard business constraints—such as fairness and coverage—in recommender systems, which are often softened into penalty terms in existing approaches, leading to frequent violations in deployment. To overcome this limitation, the authors propose DualAgent-Rec, a novel framework that leverages a large language model (LLM) as a coordinator to orchestrate two collaborative agents: one optimizes recommendation accuracy under strict adherence to hard constraints, while the other enhances diversity through unconstrained Pareto search. An adaptive epsilon-relaxation mechanism is integrated to ensure solution feasibility and computational efficiency. Evaluated on the Amazon Reviews 2023 dataset, the method achieves 100% constraint satisfaction, improves Pareto hypervolume by 4–6%, and maintains an excellent trade-off between accuracy and diversity.
📝 Abstract
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.