# Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
## 2 Related Work  
### Knowledge-Aware Recommendation  
Embedding-based. Embedding-based methods [9; 10; 11; 33; 34] leverage relations and entities in the KG to enhance the semantic representations in recommender systems [14]. The earliest works trace back to CKE [11] which incorporates different types of side information into the collaborative filtering framework and the embedding of items' structural knowledge is encoded with TransR [13]. Another representative solution is KTUP [10], which employs a joint learning mechanism of KG completion and item recommendation. Specifically, TransH [35] is utilized to learn distributional representations of the KG, and a hyperplane-based translation component is designed to handle user preferences. Multi-Rec [34] proposes a multi-task learning framework that combines CF-based and embedding-based to simultaneously learn user features in UIG and item features in KG. MFPRKG [8] proposes a multi-feature augmented model that not only embeds structured information from the KG, but also fuses temporal and textual information. However, one problem shared by embedding-based methods lies in their incapacity to handle higher-order connectivities, coupled with a lack of interpretability for recommendation.  
Path-based. Path-based methods [4; 16; 17; 36; 37; 38] are devoted to exploring the high-order connectivity information within the KG to guide the recommendation. They are explicable as the designed meta-paths guide the recommendation process based on their comparison between the candidate item and the historical interactions. For example, PER [38] pre-defines various types of meta-paths and subsequently extracts all paths that conform to the defined meta-path for each user-item pair. MCRec [4] uses CNN to explicitly embed user-item pairs to obtain the representation of meta-paths. Additionally, KPRN [16] employs LSTM to generate path representations by integrating the semantics of entities and relations. PGPRs [36] design a series of quantitative properties to explain reasoning paths from the user's perspective. Despite the interpretability of successful recommendation, a deficiency is that these methods highly depend on manually designed meta-paths. This makes them insufficiently scalable to other domain knowledge and overly reliant on human efforts.  
GNN-based. GNN-based methods [18; 19; 21; 23; 39; 40; 41] are emerging, which can be attributed to their compatibility with the graph structure inherent in recommendation data and recursively propagate the embeddings to capture multi-hop neighbors. For instance, KGAT [19] leverages the relation-aware aggregation mechanisms to model high-order relations effectively. KGCN [18] proposes a mini-batch training strategy and integrates neighbor information with bias to update KG entity representations, allowing for a better exploration of the local proximity structure. KGNN-LS [39] learns the item representations based on the designed user-specific weights to different relations. KGIN [23] is a state-of-the-art method that models user intents for relations and employs relational path-aware aggregation to effectively capture rich information on the KG.