# Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
## 1 Introduction  
Recommender systems play a crucial role in helping users discover interest items by analyzing their historical preferences and behaviors, addressing the challenge of information overload caused by the exponential growth of data on the Internet [1]. These systems have become widely applied in various domains, including e-commerce platforms [2], social websites [3], and music streaming services [4]. Among contemporary approaches, collaborative filtering (CF) [5, 6] is a typical framework that leverages users' behavioral data to measure user-item similarity, leading to effective recommendation services. However, CF-based methods heavily rely on historical user-item interactions, making them inadequate for addressing challenges like data sparsity and cold start, thus restricting the accurate predictions of user preference. To overcome these limitations, we incorporate the structured knowledge graph (KG) into recommender systems. The KG offers additional item-wise connectivity information beyond the user-item interaction graph (UIG), as well as provides fine-grained attribute features, facilitating the capture of the true relatedness behind user behavior and interests [7, 8]. The KG serves as a valuable resource, containing rich factual information about items and their relationships. By introducing meaningful inherent semantic relatedness into user behavior data, we generate high-quality user and item representations, mitigating data sparsity and ensuring better recommendation performance. The prevalent KG-aware methods can be divided into three categories: embedding-based, path-based, and GNN-based. Specifically, early embedding-based methods [8; 9; 10; 11] leverage knowledge graph embedding algorithms, such as TransE [12] and TransR [13], to encode entities as vector representations, subsequently integrating them into the recommendation framework. Despite being effective, the aforementioned methods suffer from the deficiency of exploiting the high-order connection within the KG, failing to provide reasonable explanations about user preferences [14]. To capture multi-hop interactions between users and items, the path-based methods enhance representations by considering the meaningful long-range structure within KG [15; 16; 17]. This structure serves as propagation paths for user preferences, providing an intuitive demonstration of interpretability. Nevertheless, these methods rely on manually constructed meta paths, demanding substantial human effort and exhibiting poor scalability. Recently, with the rise of graph neural network (GNN), there has been a strong research interest in leveraging GNN for recursive information propagation among KG nodes, capturing high-order information [18; 19; 20; 21; 22]. Although these methods have shown strong performance, the integration of KG also introduces novel challenges, particularly concerning data sparsity and the long-tail issue inherent in the KG [23]. To address the scarcity of the supervision signal in sparse data scenarios, self-supervised learning (SSL) [24], which can be generally categorized into generative and contrastive, has garnered significant attention as an emerging learning paradigm capable of reducing dependency on manual labels. Several recent SSL-based methods [25; 26] leverage different data augmentation strategies to construct contrasting views of the UIG, thereby exploiting the intrinsic structure and patterns within the UIG data to enhance recommendation performance. Furthermore, SSL-based methods that incorporate KG, such as KGCL [27], KGTN [28] and HCVL [29], aim to create contrasting views within both the KG and UIG data, enabling items and users to learn from the rich semantic and contextual information embedded in the KG. KAUR [30] unifies UIG and KG as a collaborative knowledge graph and treats neighbor nodes as contrastive positive pairs. KGRec [31] proposes a unified framework that combines two self-supervised learning paradigms, namely generative and contrastive learning.