# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 5 Experiment
### Experiment Results
#### 5.2.2 Cold-Start Analysis for Different Number of Check-Ins  
In the LSBN, the cold-start problem has consistently been a factor in reducing model performance. Due to the lack of check-in data from relevant users, recommendations for these users are generally unsatisfactory. To address this issue, this paper categorizes users into three groups based on their check-in frequency in the training set: active, normal, and inactive. The top 30% of users with the most check-ins are classified as active, the  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & NYC & & & \multicolumn{3}{c}{TKY} & \multicolumn{3}{c}{CA} \\ \hline  & Acc@1 & Acc@5 & Acc@10 & MRR & Acc@1 & Acc@5 & Acc@10 & MRR & Acc@1 & Acc@5 & Acc@10 & MRR \\ \hline FPMC & 0.1003 & 0.2126 & 0.2970 & 0.1701 & 0.0814 & 0.2045 & 0.2746 & 0.1344 & 0.0383 & 0.0702 & 0.1159 & 0.0911 \\ \hline LSTM & 0.1305 & 0.2719 & 0.3283 & 0.1857 & 0.1335 & 0.2728 & 0.3277 & 0.1834 & 0.0665 & 0.1306 & 0.1784 & 0.1201 \\ \hline PRME & 0.1159 & 0.2236 & 0.3105 & 0.1712 & 0.1052 & 0.2278 & 0.2944 & 0.1786 & 0.0521 & 0.1034 & 0.1425 & 0.1002 \\ \hline STGCN & 0.1799 & 0.3425 & 0.4279 & 0.2788 & 0.1716 & 0.3453 & 0.3927 & 0.2504 & 0.0961 & 0.2907 & 0.2613 & 0.1712 \\ \hline PLSPL & 0.1917 & 0.3678 & 0.4523 & 0.2806 & 0.1889 & 0.3523 & 0.4150 & 0.2542 & 0.1072 & 0.2278 & 0.2995 & 0.1847 \\ \hline STAN & 0.2231 & 0.4582 & 0.5734 & 0.3253 & 0.1963 & 0.3798 & 0.4464 & 0.2852 & 0.1104 & 0.2348 & 0.3018 & 0.1869 \\ \hline GETNext & 0.2435 & 0.5089 & 0.6143 & 0.3621 & 0.2254 & 0.4417 & 0.5287 & 0.3262 & 0.1357 & 0.2852 & 0.3590 & 0.2103 \\ \hline STHGCN & 0.2734 & 0.5361 & 0.6244 & 0.3915 & **0.2950** & 0.5207 & 0.5980 & 0.3986 & 0.1730 & 0.3529 & 0.4191 & 0.2558 \\ \hline
**ours** & **0.2820** & **0.6092** & **0.7039** & **0.4239** & 0.2934 & **0.5642** & **0.6361** & **0.4163** & **0.1788** & **0.3651** & **0.4258** & **0.2602** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison between HGSL-POI and baselines.  
bottom 30% with the fewest check-ins are classified as inactive, and the remaining users are categorized as normal. The quantile-based split method can help discriminate users' different behaviors. The Table 3 presents the specific results.  
Since STHGCN is the most effective model among the baseline models, we use STHGCN as the baseline for measuring user cold-start problem. The results in Table 2 show that our model significantly improves the performance of inactive users, indicating its ability to provide more accurate recommendations for users with fewer check-ins. This may be due to the collaborative information from other users. The results on the active users imply that too many check-ins may yield the opposite effect. While the Acc@1 for predicting normal and active users in NYC is slightly lower than the baseline model, the MRR performance is comparable, demonstrating that our model can still achieve good results. Moreover, we can see that the results in TKY is larger than NYC. This may be because of the increasing number of sequences, making it extract more collaborative information from other sequences.