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是缓解数据稀疏性的有效方法。大多数现有的知识感知方法通常通过枚举图邻居来执行递归嵌入传播。然而，随着跳数的增加，节点的邻居数量呈指数增长，迫使节点在此递归传播中了解到庞大的邻居以提取高阶语义相关性。这可能会给推荐带来更多有害的噪声而不是有用的信息，导致所学习的节点表示彼此难以区分，即众所周知的过度平滑问题。为了缓解这个问题，我们提出了一种基于分层和对比学习的知识感知推荐框架HiCON。具体而言，为了避免邻居的指数增长，我们提出了分层信息聚合机制，分别与低阶邻居和元路径约束的高阶邻居交互。此外，我们还执行跨阶对比学习，以使表示更具区分性。三个数据集上的广泛实验表明，HiCON相对于现有方法具有显着的优越性。