# Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
## 5 Experiments
### Performance Comparison (RQ1)  
The performance of our HKRec is first compared with baselines based on Recall@20 and NDCG@20. The comparison results are presented in Table 2 where the best baselines are highlighted with underlined and the best performances with bold. We can find the following:  
* The proposed HKRec consistently outperforms all baselines across all three datasets, demonstrating its superiority in recommendation performance. Particularly noteworthy is that HKRec surpasses even the best-performing baseline. Recall@20 and NDCG@20 improved by 2.2% to 24.95% and 3.38% to 22.32% in the Last-FM dataset, by 7.0% to 23.82% and 5.7% to 39.66% in the MIND dataset, and by 1.76% to 34.73% and 1.62% to 35.13% in the Alibaba-iFashion dataset. We summarize the reasons for the usefulness of our HKRec as follows: (1) The design of T-GMAE effectively extracts semantic relatedness within the items that contribute to the recommendation tasks. (2) The incorporation of multiple contrastive cross-views assists HKRec in capturing deeper semantic relatedness and injecting entity representations into the user-item interaction.
* Examining Tables 1 and 2, it can be seen that when the KG contains more diverse entities and relations, our HKRec model, through its T-GMAE and cross-view contrastive learning mechanisms, can effectively discover more useful information from the KG. This process enriches the model with semantic information to understand and recommend item. Consequently, the model achieves the highest recall of 0.1192 on the Alibaba-iFashion dataset. In addition, compared with the strongest baseline, the recall improved by 2.2%, 7.0%, and 1.76% on different datasets, respectively. The model achieves the most notable performance improvement on the MIND dataset, which has the most sparse interactions. In such cases, the provision of more diverse information from the KG has the most significant impact.
* The knowledge-aware methods outperform CF-based methods, underscoring the effective alleviation of data sparsity issues in recommender systems through the incorporation of KG.
* The performance of the GNN-based methods (i.e, KGAT, KGIN) on the Last-FM and Alibaba-iFashion datasets outperforms the embedding-based approach, CKE. This outcome highlights the effectiveness of GNNs in capturing higher-order dependencies and attaining superior knowledge representations. However, such superiority is not observed in the MIND dataset. The rationale lies in its relatively small knowledge graph scale, where entities exhibit low connection density and lack high-order dependencies. Consequently, the efficacy of GNNs in this scenario is limited.
* The two methods combining both contrastive and generative paradigms (i.e., KGRec, HKRec) demonstrate superior performance compared to others that employ a single contrastive learning paradigm (i.e., SGL, KGCL) only. This observation indicates that the generative facilitates the distilling of richer semantic information from the KG. Hence, leveraging this paradigm effectively is crucial for enhancing recommendation performance.