# Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
## 1 Introduction
These methods learn user and item representations from the available original data autonomously. As we can see, SSL-based methods for recommendation mitigate sparsity and cold-start issues by eliminating model dependence on large volumes of labeled data. Even with the success of the methods mentioned above, most SSL-based efforts in knowledge-aware recommendation typically focus on uniform frameworks for learning representations, with limited exploration into hierarchical learning. They commonly employ contrastive learning paradigms, either by constructing multiple contrasting views between the KG and UIG (e.g., HCVL [29]) or by introducing cross-knowledge graph structures to form multiple contrasting views (e.g., KGIN [23], MCCLK [32]). In the process of graph augmentation, they particularly emphasize edge-level disruption and learning, over-emphasizing the pair-wise topology structure, which refers the one-to-one connections between nodes. As a result, they overlook the richness of the KG structure, especially the important semantic and structural information inherent in neighborhood-wise connections--the many-to-one connections where a node is linked to multiple neighboring nodes. This limitation restricts their ability to deeply explore the neighborhood-wise semantic connections between KG entities and UIG nodes. It is noteworthy that the coarse-grained semantic understanding can result in the generation of low-quality user-item interaction representations, negatively impacting recommendation performance. To overcome this limitation, we emphasize a comprehensive strategy that sufficiently processes KG facts and UIG data in a more effective and fine-grained manner. We capture implicit relevant relatedness between entities and items, obtaining high-quality representations for recommendation. Therefore, we propose a method named Hierarchical self-supervised learning for Knowledge-aware Recommendation (HKRec), which seeks to maximize the utilization of explicit hierarchical knowledge to acquire valuable implicit semantics. This enables the model to enhance user-item representations from two perspectives. First, we introduce the Triple-Graph Masked Autoencoder (T-GMAE) designed to better reveal the representation of inherent semantic relations within the KG. T-GMAE achieves this by hierarchically reasoning the masked node features, connections, and degrees. Second, we incorporate contrastive learning in a hybrid manner, employing both neighborhood-level and edge-level augmentation in a parallel way. This bridges the latent information mining of the KG with the user-item interaction modeling via two augmentation operators to achieve more comprehensive information distillation. The parallel contrastive learning paradigm is intended to distill auxiliary supervision signals, ensuring that the model can effectively integrate information hierarchically for improved entity-item alignment. In summary, we outline the contributions of HKRec as follows:  
* We highlight the significance of the hierarchical learning mechanism in both KG and UIG for knowledge graph-aware recommendation within a joint self-supervised learning paradigm. This is crucial as it generates more valuable self-supervised signals for item and user representations, enhancing the ability to mine useful knowledge in scenarios of data sparsity. * We present a model HKRec to unify the generative and the contrastive learning paradigms in a hierarchical manner. HKRec captures the implicit semantic information from the knowledge graph incorporating a multi-perspective hierarchy and integrates representations into recommender systems through hierarchical entity-item alignments. * We perform extensive experiments on three real-world benchmark datasets to verify that HKRec achieves significant performance compared to recent state-of-the-art baselines. The remainder of the paper is organized as follows. Section 2 summarizes the related works. Section 3 formally presents our studied task.