## 5 Results and Analysis  
### Performance Comparison  
We present a comparative analysis of the prediction accuracy of our proposed model against several baseline methods. The results of the comparison experiment are shown in Tables 3 and 4. For both datasets, the overall performance indicates that traditional methods such as FPMC and PRME have lower recall rates across all metrics, whereas modern methods such as STAN, GETNext, and STHGCN show significant improvements. This performance gap highlights the evolution of POI recommendation techniques, where earlier matrix factorization approaches struggle to capture the complex spatio-temporal dependencies that characterize human mobility patterns. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Time per Epoch (s)** & **Total Time (min)** & **Peak GPU Memory (GB)** \\ \hline NYC & \(\approx\)240 & \(\approx\)80 & 5.90 \\ TKY & \(\approx\)1053 & \(\approx\)370 & 9.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computational efficiency of our model on NYC and TKY datasets. In general, our HGL outperforms all others across all metrics. Specifically, our method shows a notable improvement over STHGCN, achieving a 3.01% increase in Recall@5 and 1.59% increase in Recall@10 on the NYC dataset, and improvements of 2.57% and 1.35% on the TKY dataset, respectively. The more substantial improvements in Recall@5 compared to Recall@10 suggest that our hierarchical approach is particularly effective at identifying the most relevant POIs within a smaller recommendation set, which is crucial for practical applications where users typically focus on the top few recommendations. Additionally, in terms of MRR, our approach outperforms STHGCN by 2.41% on the NYC dataset and shows a 2.69% improvement on the TKY dataset. This signifies that our method not only identifies the correct POI more frequently but also ranks it higher among the recommendations. The consistent improvement in MRR across both datasets demonstrates our model's ability to better prioritize the most relevant POIs, which directly enhances the user experience in real-world recommendation scenarios. When examining the performance patterns across different methods, we have observed that the improvement margin between sequential models (LSTM, PLSPL) and graph-based models (GETNext, STHGCN) is substantially larger than the gap between traditional methods (FPMC, PRME) and sequential models. This trend indicates that graph structures are particularly well suited for capturing the complex relationships in POI recommendation tasks. Our hierarchical graph approach further extends this advantage by modeling these relationships at multiple scales. Interestingly, the performance improvements differ slightly between the NYC and TKY datasets. The NYC dataset shows larger improvements in Recall@5, while the TKY \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **Recall@5** & **Recall@10** & **MRR** \\ \hline FPMC & 0.2045 & 0.2746 & 0.1344 \\ PRME & 0.2278 & 0.2944 & 0.1786 \\ LSTM & 0.2721 & 0.3281 & 0.1835 \\ PLSPL & 0.3523 & 0.4150 & 0.2542 \\ STAN & 0.3772 & 0.4357 & 0.2598 \\ GETNext & 0.4129 & 0.5086 & 0.3104 \\ STHGCN & 0.5207 & 0.5980 & 0.3986 \\ \hline HGL (Ours) & **0.5341** & **0.6061** & **0.4093** \\ Improvement & 2.57\% & 1.35\% & 2.69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of a comparison between the Recall@k and MRR values of different methods on the TKY dataset. Numbers marked in bold and underlined text represent the best and second-best results, respectively.