Collaborative filtering (CF) is one of the dominant techniques used in modern recommender systems. Traditional CF-based methods suffer from issues of data sparsity and cold start. Therefore, side information has been widely utilized by researchers to address these problems. Most side information is typically heterogeneous and in the form of the graph structure. In this work, we propose a deep end-to-end recommendation framework named GSIRec to make full use of the graph side information. Specifically, GSIRec derives a multi-task learning approach that introduces a side information task to assist the recommendation task. The key idea is that we design a delicate knowledge assistance module to be the bridge between tasks, which captures useful knowledge to complement each task. Also, we utilize a graph attention method to exploit the topological structure of side information to enhance recommendation. To show the wide application and flexibility of our framework, we integrate side information from two aspects: social networks (for users) and knowledge graphs (for items). We apply GSIRec in two recommendation scenarios: social-aware recommendation and knowledge-aware recommendation. To evaluate the effectiveness of our framework, we conduct extensive experiments with four real-world public datasets. The results reveal that GSIRec consistently outperforms the state-of-the-art methods on the rating prediction task and top-K recommendation task. Moreover, GSIRec can alleviate data sparsity and cold start issues to some extent.

协同过滤 (CF) 是现代推荐系统中使用的主要技术之一。传统的基于 CF 的方法存在数据稀疏和冷启动的问题。因此，研究人员广泛利用辅助信息来解决这些问题。大多数辅助信息通常是异构的，并以图结构的形式存在。在这项工作中，我们提出了一个名为 GSIRec 的深度端到端推荐框架，以充分利用图边信息。具体来说，GSIRec 衍生出一种多任务学习方法，该方法引入了辅助信息任务来辅助推荐任务。关键思想是我们设计了一个精致的知识辅助模块成为任务之间的桥梁，它捕获有用的知识来补充每个任务。此外，我们利用图注意力方法来利用边信息的拓扑结构来增强推荐。为了展示我们框架的广泛应用和灵活性，我们从两个方面整合了辅助信息：社交网络（对于用户）和知识图谱（对于物品）。我们将 GSIRec 应用于两种推荐场景：社交感知推荐和知识感知推荐。为了评估我们框架的有效性，我们对四个真实世界的公共数据集进行了广泛的实验。结果表明，GSIRec 在评级预测任务和 top-K 推荐任务上始终优于最先进的方法。此外，GSIRec 可以在一定程度上缓解数据稀疏和冷启动问题。