Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, which defines the neighbors being aggregated based on user-item interactions, can be noisy for users/items with abundant interactions and insufficient for users/items with scarce interactions. Additionally, the adjacency matrix ignores user-user and item-item correlations, which can limit the scope of beneficial neighbors being aggregated. In this work, we propose a new graph adjacency matrix that incorporates user-user and item-item correlations, as well as a properly designed user-item interaction matrix that balances the number of interactions across all users. To achieve this, we pre-train a graph-based recommendation method to obtain users/items embeddings, and then enhance the user-item interaction matrix via top-K sampling. We also augment the symmetric user-user and item-item correlation components to the adjacency matrix. Our experiments demonstrate that the enhanced user-item interaction matrix with improved neighbors and lower density leads to significant benefits in graph-based recommendation. Moreover, we show that the inclusion of user-user and item-item correlations can improve recommendations for users with both abundant and insufficient interactions. The code is in \url{https://github.com/zfan20/GraphDA}.

翻译：图协同过滤（GCF）是推荐系统中捕捉高阶协同信号的主流技术。然而，GCF基于用户-物品交互定义邻居聚合的二部邻接矩阵，对于交互丰富的用户/物品存在噪声问题，对于交互稀疏的用户/物品存在信息不足问题。此外，该邻接矩阵忽略了用户-用户和物品-物品之间的相关性，从而限制了可聚合的有益邻居范围。本文提出一种新型图邻接矩阵，该矩阵融合了用户-用户与物品-物品相关性，同时设计了平衡所有用户交互数量的用户-物品交互矩阵。具体实现中，我们通过预训练基于图的推荐方法获取用户/物品嵌入，并采用Top-K采样增强用户-物品交互矩阵；同时将对称的用户-用户和物品-物品相关性分量扩充至邻接矩阵。实验表明，增强后的用户-物品交互矩阵具有更优的邻居结构及更低的矩阵密度，显著提升了基于图的推荐性能。此外，我们证明引入用户-用户与物品-物品相关性可同时改善交互丰富用户与交互稀疏用户的推荐效果。代码开源于\url{https://github.com/zfan20/GraphDA}。