Recommender systems frequently encounter data sparsity issues, particularly when addressing cold-start scenarios involving new users or items. Multi-source cross-domain recommendation (CDR) addresses these challenges by transferring valuable knowledge from multiple source domains to enhance recommendations in a target domain. However, existing reinforcement learning (RL)-based CDR methods typically rely on a single-agent framework, leading to negative transfer issues caused by inconsistent domain contributions and inherent distributional discrepancies among source domains. To overcome these limitations, MARCO, a Multi-Agent Reinforcement Learning-based Cross-Domain recommendation framework, is proposed. It leverages cooperative multi-agent reinforcement learning, where each agent is dedicated to estimating the contribution from an individual source domain, effectively managing credit assignment and mitigating negative transfer. In addition, an entropy-based action diversity penalty is introduced to enhance policy expressiveness and stabilize training by encouraging diverse agents' joint actions. Extensive experiments across four benchmark datasets demonstrate MARCO's superior performance over state-of-the-art methods, highlighting its robustness and strong generalization capabilities. The code is at https://github.com/xiewilliams/MARCO.

翻译：推荐系统常面临数据稀疏性问题，尤其是在处理涉及新用户或新项目的冷启动场景时。多源跨域推荐通过从多个源域迁移有价值知识来增强目标域的推荐效果，从而应对这些挑战。然而，现有的基于强化学习的跨域推荐方法通常依赖单智能体框架，导致因域贡献不一致及源域间固有分布差异而产生的负迁移问题。为克服这些限制，本文提出了MARCO——一种基于多智能体强化学习的跨域推荐框架。该框架采用协作式多智能体强化学习，其中每个智能体专门评估单个源域的贡献，有效管理信用分配并缓解负迁移。此外，框架引入了基于熵的动作多样性惩罚机制，通过鼓励多样化的智能体联合行动来增强策略表达能力并稳定训练过程。在四个基准数据集上的大量实验表明，MARCO的性能优于现有最先进方法，凸显了其鲁棒性和强大的泛化能力。代码发布于https://github.com/xiewilliams/MARCO。