Incorporating knowledge graph into recommendation is an effective way to alleviate data sparsity. Most existing knowledge-aware methods usually perform recursive embedding propagation by enumerating graph neighbors. However, the number of nodes' neighbors grows exponentially as the hop number increases, forcing the nodes to be aware of vast neighbors under this recursive propagation for distilling the high-order semantic relatedness. This may induce more harmful noise than useful information into recommendation, leading the learned node representations to be indistinguishable from each other, that is, the well-known over-smoothing issue. To relieve this issue, we propose a Hierarchical and CONtrastive representation learning framework for knowledge-aware recommendation named HiCON. Specifically, for avoiding the exponential expansion of neighbors, we propose a hierarchical message aggregation mechanism to interact separately with low-order neighbors and meta-path-constrained high-order neighbors. Moreover, we also perform cross-order contrastive learning to enforce the representations to be more discriminative. Extensive experiments on three datasets show the remarkable superiority of HiCON over state-of-the-art approaches.

翻译：将知识图谱融入推荐系统是缓解数据稀疏性的有效方法。现有知识感知方法通常通过枚举图邻居进行递归嵌入传播。然而，随着跳数增加，节点邻居数量呈指数增长，迫使节点在此递归传播过程中感知大量邻居，以提取高阶语义关联性。这可能导致推荐引入更多有害噪声而非有用信息，使得学习到的节点表示难以相互区分，即著名的过平滑问题。为缓解该问题，我们提出了一种名为HiCON的知识感知推荐分层对比表示学习框架。具体而言，为避免邻居的指数级膨胀，我们提出分层消息聚合机制，分别与低阶邻居及元路径约束的高阶邻居进行交互。此外，我们还进行跨阶对比学习，使表示更具判别性。在三个数据集上的大量实验表明，HiCON相较于现有最先进方法具有显著优越性。