<p>Graph neural network-based recommendation models have achieved remarkable results by performing information propagation on a bipartite graph consisting of user nodes and item nodes. Nevertheless, these models typically focus on message passing between nodes iteratively, and pay insufficient attention to the intricate interactions encapsulated by the graph’s edges, which play a pivotal role in recommendation systems. Moreover, the process of node embedding learning often fails to consider the potential similarities between users or items. Such omission could curtail the representational learning efficacy of these models. To address these challenges, we propose an edge-based graph neighbor filtering network framework for recommendation. This framework introduces an edge-based message passing mechanism to capture the interaction information between users and items. Additionally, a penalty term that utilizes neighbor filtering is incorporated to coalesce nodes exhibiting analogous characteristics more proximally within the embedding space. This inclusion serves as a catalyst for augmenting the prowess of representation learning. Comprehensive experiments and ablation studies are conducted, and the results validate the effectiveness of our framework. For example, on the datasets book_crossing and df_modcloth, the proposed framework can achieve relative improvements of 3% and over 20%, respectively, compared to the strongest baseline in terms of NDCG@10 and Recall@10.</p>