Graph Neural Networks (GNNs) provide powerful representations for recommendation tasks. GNN-based recommendation systems capture the complex high-order connectivity between users and items by aggregating information from distant neighbors and can improve the performance of recommender systems. Recently, Knowledge Graphs (KGs) have also been incorporated into the user-item interaction graph to provide more abundant contextual information; they are exploited to address cold-start problems and enable more explainable aggregation in GNN-based recommender systems (GNN-Rs). However, due to the heterogeneous nature of users and items, developing an effective aggregation strategy that works across multiple GNN-Rs, such as LightGCN and KGAT, remains a challenge. In this paper, we propose a novel reinforcement learning-based message passing framework for recommender systems, which we call DPAO (Dual Policy framework for Aggregation Optimization). This framework adaptively determines high-order connectivity to aggregate users and items using dual policy learning. Dual policy learning leverages two Deep-Q-Network models to exploit the user- and item-aware feedback from a GNN-R and boost the performance of the target GNN-R. Our proposed framework was evaluated with both non-KG-based and KG-based GNN-R models on six real-world datasets, and their results show that our proposed framework significantly enhances the recent base model, improving nDCG and Recall by up to 63.7% and 42.9%, respectively. Our implementation code is available at https://github.com/steve30572/DPAO/.

翻译：图神经网络（GNN）为推荐任务提供了强大的表示能力。基于GNN的推荐系统通过聚合远距离邻居的信息，捕捉用户与物品之间复杂的高阶连通性，从而提升推荐系统性能。近年来，知识图谱（KG）也被融入用户-物品交互图中，以提供更丰富的上下文信息；它们被用于解决冷启动问题，并在基于GNN的推荐系统（GNN-R）中实现更具可解释性的聚合。然而，由于用户和物品的异质性，开发一种适用于多种GNN-R（如LightGCN和KGAT）的有效聚合策略仍然是一个挑战。本文提出了一种新颖的基于强化学习的推荐系统消息传递框架，我们称之为DPAO（面向聚合优化的双策略框架）。该框架通过双策略学习，自适应地确定聚合用户和物品的高阶连通性。双策略学习利用两个深度Q网络模型，从GNN-R中挖掘用户感知和物品感知的反馈，从而提升目标GNN-R的性能。我们使用非基于KG和基于KG的GNN-R模型在六个真实世界数据集上评估了所提出的框架，结果表明，该框架显著增强了最新的基础模型，使nDCG和Recall指标分别最高提升了63.7%和42.9%。我们的实现代码可在https://github.com/steve30572/DPAO/获取。