## 4 Experiments
### Experiment Results  
As shown in Table 2, the model proposed in this study performed optimally in all five evaluation indicators compared to the baseline models. The evaluation indicators improved approximately 7-10%. In the baseline model, self-attention models like the STAN demonstrated superior performances compared to RNN-based models, such as the ST-RNN. This is attributed to the self-attention mechanism's ability to compute attention weights for all positions in the input sequence simultaneously, facilitating the efficient utilization of computational resources, particularly when processing lengthy sequences. The traditional MF model is limited in terms of capturing the temporal pattern and temporal dynamics in the sequence of interest points, so it cannot effectively deal with the time-dependent relationship and spatial-temporal information in the sequence. While the FPMC model takes into account user preferences and contextual information in the sequence, its Markov assumption-based structure may not fully capture long-term time dependencies, resulting in an inadequate long-term predictive performance. ST-RNNs and LSTMs may face computational and storage challenges when dealing with large-scale spatial and temporal data, and modeling long-term dependencies may also be inadequate, resulting in degraded predictive performances over long time series. The introduction of the multi-head attention mechanism in the ATST-LSTM model represents a significant advancement, as it enables the model to automatically assess the relevance of various inputs to the network at each step and adjust the attention weights of the inputs accordingly. This mechanism is highly beneficial to enhancing the model's comprehension and utilization of spatiotemporal contextual information, thereby improving the accuracy of the subsequent POI prediction. However, in contrast to our model, the ATST-LSTM does not appear to fully consider the impact of additional information, such as categories, on the POI prediction accuracy. Category information plays a pivotal role in recommender systems, as it can provide valuable insights into user preferences and POI characteristics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**NYC**} \\ \hline  & **Acc@1** & **Acc@5** & **Acc@10** & **Acc@20** & **MRR** \\ \hline MF & 0.0368 & 0.0961 & 0.1522 & 0.2375 & 0.0672 \\ FPMC & 0.1003 & 0.2126 & 0.2970 & 0.3323 & 0.1701 \\ ST-RNN & 0.1483 & 0.2923 & 0.3622 & 0.4502 & 0.2198 \\ LSTM & 0.1305 & 0.2719 & 0.3283 & 0.3568 & 0.1857 \\ ATST-LSTM & 0.1525 & 0.3208 & 0.3985 & 0.4816 & 0.2363 \\ STGN & 0.1716 & 0.3381 & 0.4122 & 0.5017 & 0.2598 \\ STGCN & 0.1799 & 0.3425 & 0.4279 & 0.5214 & 0.2788 \\ STAN & 0.2231 & 0.4582 & 0.5734 & 0.6328 & 0.3253 \\ \hline Ours & 0.2378 & 0.5284 & 0.6304 & 0.6883 & 0.3665 \\ \hline  & \multicolumn{4}{c}{**TKY**} \\ \hline  & **Acc@1** & **Acc@5** & **Acc@10** & **Acc@20** & **MRR** \\ \hline MF & 0.0241 & 0.0701 & 0.1267 & 0.1845 & 0.0861 \\ FPMC & 0.0814 & 0.2045 & 0.2746 & 0.3450 & 0.1344 \\ ST-RNN & 0.1409 & 0.3022 & 0.3577 & 0.4753 & 0.2212 \\ LSTM & 0.1335 & 0.2728 & 0.3277 & 0.3598 & 0.1834 \\ ATST-LSTM & 0.1596 & 0.3283 & 0.3642 & 0.4473 & 0.2408 \\ STGN & 0.1689 & 0.3391 & 0.3848 & 0.4514 & 0.2422 \\ STGCN & 0.1716 & 0.3453 & 0.3927 & 0.4763 & 0.2504 \\ STAN & 0.1963 & 0.3798 & 0.4464 & 0.5119 & 0.2852 \\ \hline Ours & 0.1992 & 0.4371 & 0.5260 & 0.5981 & 0.3092 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Acc @K and MRR performance comparison of two datasets. Models such as the STGN, STGCN, and STAN may encounter computational and storage bottlenecks when dealing with large amounts of data and may be limited by the model complexity and training efficiency when considering complex spatial and temporal dependencies. Therefore, while these models have achieved some results in next-POI recommendation, there is still room for improvement.