# Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
## 5 Experiments
### Ablation Study (RQ2)  
Impact of self-supervised learning strategies. To study the effects of two modules, T-GMAE and cross-view contrastive learning, we compare the result of HKRec with the designed two variants and the KGRec as follows:  
* w/o ND: We remove the neighborhood-level dropout components from the contrastive learning module. * w/o T-GMAE: We remove the T-GAME module which includes the degree decoder component and the node feature masking reconstruction component. The performance comparison results are presented in Table 3. It is observed that, compared to the best-performing baseline, each of the self-supervised designs makes a clear contribution to the overall performance. HKRec, which utilizes T-GMAE and parallel multi cross-view contrastive learning simultaneously, demonstrates optimal performance. It is worth noting that compared to solely removing the neighborhood-level dropout component in the contrastive learning module, the performance deterioration observed upon removing two decoders about degrees and features from T-GMAE suggests a more significant contribution of T-GMAE to performance enhancement. This can be attributed to T-GMAE's adoption of a hierarchical triple masking reconstruction strategy, encompassing both structure-level and node-level information. This facilitates a more comprehensive exploration of semantics within the KG. In general, these findings underscore the importance of hierarchical learning in enhancing model performance. Hyperparameter sensitivit. We investigate the sensitivity of the performance to the key hyperparameters, namely the connection masking rate \(\varphi\), the feature masking rate \(\mu\), and the node dropout rate \(\rho\), which control the intensity of self-supervised learning. For these \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Last-FM**} & \multicolumn{2}{c}{**MIND**} & \multicolumn{2}{c}{**Alibaba-iFashion**} \\ \cline{2-7}  & **Recall** & **NDCG** & **Recall** & **NDCG** & **Recall** & **NDCG** \\ \hline BPR [56] & 0.0847 & 0.0720 & 0.0392 & 0.0264 & 0.0821 & 0.0506 \\ LightGCN [41] & 0.0716 & 0.0644 & 0.0403 & 0.0279 & 0.0999 & 0.0614 \\ \hline CKE [11] & 0.0853 & 0.0707 & 0.0385 & 0.0276 & 0.0778 & 0.0482 \\ \hline KGAT [19] & 0.0873 & 0.0743 & 0.0339 & 0.0294 & 0.0961 & 0.0582 \\ KGIN [23] & 0.0914 & 0.0786 & 0.0382 & 0.0245 & 0.1171 & 0.0730 \\ \hline SGL [25] & 0.0768 & 0.0679 & 0.0339 & 0.0210 & 0.1118 & 0.0702 \\ \hline KGCL [27] & 0.0876 & 0.0789 & 0.0352 & 0.0221 & 0.1097 & 0.0693 \\ KGRec [31] & 0.0933 & 0.0801 & 0.0414 & 0.0328 & 0.1170 & 0.0731 \\ \hline HKRec & **0.0954** & **0.0829** & **0.0445** & **0.0348** & **0.1192** & **0.0743** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparisons of different methods on Last-FM, MIND, and Alibaba-iFashion. The best results are bolded and the second-best are underlined. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**MODEL**} & \multicolumn{2}{c}{**MIND**} & \multicolumn{2}{c}{**Alibaba-iFashion**} \\ \cline{2-5}  & **Recall** & **NDCG** & **Recall** & **NDCG** \\ \hline HKRec & **0.0445** & **0.0348** & **0.1192** & **0.0743** \\ \hline w/o ND & 0.0438 & 0.0346 & 0.1185 & 0.0741 \\ w/o T-GMAE & 0.0432 & 0.0334 & 0.1180 & 0.0737 \\ KGRec & 0.0414 & 0.0328 & 0.1170 & 0.0731 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study of the HKRec. The comparison model of _w/o ND_ refers to a variant obtained by removing the neighborhood-level dropout component in HKRec, whereas the model _w/o T-GMAE_ signifies the removal of the degree decoder and the masked feature decoder. The best results are bolded. three hyperparameters, we conduct experiments with varying ranges of values. Figure 3 shows the model's performance on three datasets with different values of hyperparameters. First, we observe that the best performance is achieved with the connection masking rate \(\varphi=0.25\) across all three datasets.