# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 4 Experiments
### Performance Comparison (Rq1)  
The experimental results on the three datasets are shown in Table 3, Table 4 and Table 5, respectively. Note that there were four different variants of SSTGL depending on the data augmentation and the choice of the pre-text task:  
* SSTGL(SEP): uses spatial-aware edge perturbation and non-spatio-temporal pre-text tasks. * SSTGL(TEP): uses temporal-aware edge perturbation and non-spatio-temporal pre-text task. * SSTGL(SCL): uses spatial-aware contrastive learning and non-spatio-temporal data augmentation. * SSTGL(TCL): use temporal-aware contrastive learning and non-spatio-temporal data augmentation. \begin{table}
\begin{tabular}{c c c c c} \hline  & **MF-Based** & **GNN-Based** & **GNN and SSL-Based** \\ \hline ST-unaware & NeuMF [20] & NGCF [21], DGCF [22], LightGCN [23] & SGL [12], NCL [13] \\ ST-aware & LGLMF [3], STACP [5] & GPR [9], MPGRec [24] & SSTGL(Ours) \\ \hline \end{tabular}
\end{table} 
Table 2: Category of baselines and SSTGL. In detail, we used dropedge [12] as the non-spatial-temporal data augmentation, and we defined the non-spatio-temporal pre-text task of the POI in a similar way to Equation (14). We present the best results among these variants with respect to the performance of SSTGL in the tables. The differences in performance between the variants are analyzed in the next section. Based on the experimental results, we have the following observations:  
* SSTGL outperformed all baseline methods in most cases. In particular, the relative improvements from the strongest baselines were 6.32% (Foursquare), 13.27% (Gowalla), and 9.68% (Meituan) using the Recall@50 metric. Note that SSTGL not only worked better than the existing POI recommendation methods, but also better than the existing self-supervised graph learning methods. This demonstrates the ability of our model to use self-supervised learning to alleviate the data sparsity problem in the POI recommendation task. Although MPGRec performed better in some cases, it relies on a dynamic memory module, which requires a large memory overhead. * For the baseline models, the GNN models did not always outperform the MF models, which was related to the datasets and model architectures. For example, we found that the NeuMF model performed better than some GNN-based methods for the Meituan dataset. This may be due to the low sparsity of the Meituan dataset and the more personalized interests of the users in the take-out scenario, so aggregating higher-order neighborhood information would instead reduce the performance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Recall@5** & **Recall@10** & **Recall@20** & **Recall@50** & **Map@5** & **Map@10** & **Map@20** & **Map@50** \\ \hline NeuMF & 0.0302 & 0.0497 & 0.0808 & 0.1478 & 0.0227 & 0.0211 & 0.0231 & 0.0267 \\ NGCF & 0.0308 & 0.0500 & 0.0810 & 0.1458 & 0.0235 & 0.0216 & 0.0234 & 0.0268 \\ DGCF & 0.0332 & 0.0530 & 0.0834 & 0.1477 & 0.0266 & 0.0239 & 0.0254 & 0.0288 \\ LightGCN & 0.0352 & 0.0564 & 0.0897 & 0.1593 & 0.0271 & 0.0247 & 0.0267 & 0.0305 \\ SGL & 0.0338 & 0.0557 & 0.0911 & 0.1657 & 0.0259 & 0.0240 & 0.0262 & 0.0305 \\ NCL & 0.0344 & 0.0561 & 0.0902 & 0.1631 & 0.0267 & 0.0244 & 0.0264 & 0.0305 \\ STACP & 0.0176 & 0.0302 & 0.0509 & 0.0964 & 0.0142 & 0.0131 & 0.0143 & 0.0168 \\ LGIMF & 0.0241 & 0.0398 & 0.0646 & 0.1156 & 0.0209 & 0.0188 & 0.0201 & 0.0230 \\ GPR & 0.0302 & 0.0483 & 0.0766 & 0.1310 & 0.0183 & 0.0196 & 0.0216 & 0.0238 \\ MPGRec & 0.0471 & 0.0706 & 0.1050 & 0.1718 & **0.0308** & 0.0325 & 0.0349 & 0.0377 \\ SSTGL (Ours) & **0.0511** & **0.0786** & **0.1175** & **0.1946** & 0.0292 & **0.0329** & **0.0357** & **0.0383** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overall performance of SSTGL and all baselines for Gowalla dataset, where the best results for each column are shown in bold font, and the second-place results are underlined.