## 5 Conclusions and Future Work  
In conclusion, the present study successfully demonstrated a real-time POI (point-of-interest) recommendation system leveraging social-media check-in data. The AST-PG model, which integrates a spatial module grouping POIs by geography and category, and a temporal module creating a uniform time trajectory vector, linked by a multi-attention mechanism, achieved a significant improvement in the POI recommendation accuracy, surpassing the state-of-the-art models by over 10%. This achievement demonstrates the efficacy of the AST-PG model in POI prediction and recommendation. Accurate POI recommendations have the potential to be utilized in a multitude of applications, particularly in the transportation sector. They can facilitate in-depth analyses of the travel demand, transportation modes, and user behaviors, ultimately contributing to the rational planning and optimization of transportation networks.  
As we look towards the future, we intend to explore the integration of large models into our POI recommendation system. To accomplish this, we will investigate techniques for efficiently integrating large models into our current framework while maintaining its computational efficiency and scalability. This will involve optimizing the model architecture, input representations, and training procedures to ensure that the integrated  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Acc@1** & **Acc@5** & **Acc@10** & **Acc@20** & **MRR** \\ \hline w/o GCP & 0.1829 & 0.4022 & 0.4596 & 0.5768 & 0.2984 \\ w/o Time \& Cat & 0.1962 & 0.4251 & 0.4849 & 0.5859 & 0.3118 \\ w/o multi-head attention & 0.1884 & 0.4107 & 0.4638 & 0.5809 & 0.3043 \\ Fully Connected & 0.1873 & 0.4052 & 0.4721 & 0.5957 & 0.3067 \\ Full Model & 0.1992 & 0.4371 & 0.5260 & 0.5981 & 0.3092 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study: comparing the full model with the three variants.  
system can effectively handle large volumes of social-media data and provide real-time POI recommendations. Additionally, we plan to enhance the algorithmic efficiency of our POI recommendation system by exploring techniques such as parallel processing and distributed computing. Furthermore, we will continue to refine the input and output structures of our model to further improve the prediction speed and accuracy. Lastly, we aim to incorporate a dynamic adaptation mechanism into our system to respond to real-time changes in user preferences and spatial dynamics. This mechanism will leverage updates from social-media data to continually refine the POI recommendations, ultimately leading to an improved recommendation quality over time.  
Conceptualization, H.Y. and Z.C.; methodology, H.Y. and Z.C.; software, H.Y.; validation, Z.C.; writing--original draft preparation, H.Y.; visualization, H.Y. and Z.C.; supervision, Z.C.; project administration, Z.C.; funding acquisition, Z.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Shandong Province, Grant No. ZR2022 QF144.  
Data is contained within the article.  
The authors declare no conflict of interest.