Graph Neural Networks (GNNs) have opened up a potential line of research for collaborative filtering (CF). The key power of GNNs is based on injecting collaborative signal into user and item embeddings which will contain information about user-item interactions after that. However, there are still some unsatisfactory points for a CF model that GNNs could have done better. The way in which the collaborative signal are extracted through an implicit feedback matrix that is essentially built on top of the message-passing architecture of GNNs, and it only helps to update the embedding based on the value of the items (or users) embeddings neighboring. By identifying the similarity weight of users through their interaction history, a key concept of CF, we endeavor to build a user-user weighted connection graph based on their similarity weight. In this study, we propose a recommendation framework, CombiGCN, in which item embeddings are only linearly propagated on the user-item interaction graph, while user embeddings are propagated simultaneously on both the user-user weighted connection graph and user-item interaction graph graphs with Light Graph Convolution (LGC) and combined in a simpler method by using the weighted sum of the embeddings for each layer. We also conducted experiments comparing CombiGCN with several state-of-the-art models on three real-world datasets.

翻译：图神经网络（GNNs）为协同过滤（CF）研究开辟了一条潜在路径。GNNs的核心能力在于将协同信号注入用户和物品的嵌入表示中，使得这些嵌入随后能够包含用户-物品交互信息。然而，对于协同过滤模型而言，GNNs本可以做得更好，但仍存在一些不尽如人意之处。当前方法通过一个本质上构建于GNNs消息传递架构之上的隐式反馈矩阵来提取协同信号，这种方式仅能基于相邻物品（或用户）嵌入的值来更新嵌入。通过识别用户交互历史中的相似性权重——这是协同过滤的一个关键概念——我们致力于基于用户的相似性权重构建一个用户-用户加权连接图。在本研究中，我们提出了一种推荐框架CombiGCN，其中物品嵌入仅在线性传播于用户-物品交互图上，而用户嵌入则同时在用户-用户加权连接图和用户-物品交互图上，通过轻量图卷积（LGC）进行传播，并采用一种更简单的方法——对每一层的嵌入进行加权求和——将两者结合。我们还在三个真实世界数据集上进行了实验，将CombiGCN与多种先进模型进行了比较。