  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/.
