用多智能体协作解决跨域推荐中的负迁移问题
MARCO: A Cooperative Knowledge Transfer Framework for Personalized Cross-domain Recommendations
- 每个智能体专注评估一个源域的贡献，协同优化推荐
- 在4个数据集上均优于现有方法，显著提升推荐效果
- 适合处理冷启动和数据稀疏场景的推荐系统研究者
推荐系统常面临数据稀疏问题，尤其在新用户或新物品的冷启动场景下。多源跨域推荐（CDR）通过从多个源域迁移知识来改善目标域的推荐效果。然而，现有基于强化学习的CDR方法多采用单智能体框架，易因源域贡献不一致及分布差异导致负迁移。为此，本文提出MARCO——一种基于多智能体强化学习的跨域推荐框架。每个智能体专门估计单一源域的贡献，实现有效的信用分配并缓解负迁移。此外，引入基于熵的动作多样性惩罚项，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在性能上超越当前最优方法，展现出强鲁棒性和良好泛化能力。
原文摘要 · Abstract (English)
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.
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