# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 6 Conclusions  
In this paper, we propose the model HGSL-POI, which integrates heterogeneous graph contrastive learning, graph structure learning, and sequence modeling for the next point-of-interest (POI) recommendation. Specifically, we first construct a heterogeneous graph based on users, POIs, and POI attributes. Then, we use different meta-paths to generate different meta-path-based subgraphs for users and POIs, as well as the interaction graph between users and POIs. Then, we design two kinds of contrastive learning to align the representation vectors of different nodes. They capture the relations from different perspectives. Given that manually defined graphs may contain noise or incompleteness, we use graph structure learning to refine the meta-path subgraphs. For each subgraph, we use the representation vectors derived from that subgraph and representations of the same node type from the interaction graph to refine the graph structure. Finally, we transform each sequence into a graph based on the order of the visits. Then, we propose an attention-based graph encoder to learn the users' sequence preferences and make predictions. We perform a set of experiments on three public datasets and achieve superior performance in nearly all the comparison experiments. The experiments on different user behaviors show our model can be applied to different scenarios. Moreover, we analyze the effectiveness of our proposed meta-path-based methods. It shows comparable results compared with the results from the real-world data. The method is not a substitute for real-world data, but can be valid when users' side information, like friendship, is missing. The ablation experiments confirm the effectiveness of our proposed components. There are also some limitations in the research. We can focus on designing more effective contrastive learning methods to extract the most useful information from the graph, thereby directly improving the relevance to the downstream tasks. Additionally, the cold-start problem is vital in recommendation systems. Users or POIs without any check-ins can be great challenges when constructing heterogeneous graphs and meta-path-based subgraphs. Conceptualization, J.C. and Q.L. ; methodology, J.C. and Q.L. ; software, Q.L. ; validation, J.C. and Q.L. ; data curation, Q.L. ; writing--original draft preparation, Q.L. ; writing--review and editing, Q.L. ; visualization, Q.L. ; supervision, J.C.; project administration, J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **NYC** & \multicolumn{3}{c}{**TKY**} & \multicolumn{3}{c}{**CA**} \\ \hline
**Model** & **Acc@1** & **MRR** & **Acc@1** & **MRR** & **Acc@1** & **MRR** \\ \hline whole & 0.2820 & 0.4239 & 0.2934 & 0.4163 & 0.1788 & 0.2602 \\ \hline w/o HGCL & 0.2761 & 0.4153 & 0.2803 & 0.3807 & 0.1751 & 0.2582 \\ \hline w/o InterCL & 0.2668 & 0.4045 & 0.2764 & 0.3684 & 0.1707 & 0.2512 \\ \hline w/o 1 MP & 0.2685 & 0.4058 & 0.2571 & 0.3404 & 0.1720 & 0.2589 \\ \hline w/o GSL & 0.2702 & 0.4113 & 0.2724 & 0.3684 & 0.1738 & 0.2560 \\ \hline w/o bi-att & 0.2642 & 0.4084 & 0.2687 & 0.3852 & 0.1625 & 0.2451 \\ \hline w/o extrInfo & 0.2043 & 0.2842 & 0.2341 & 0.3126 & 0.1359 & 0.1842 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation results. **Funding:** This research was funded by National Natural Science Foundation of China (61104166), and the computing power support of the Ziqiang 5000 supercomputer platform at Shanghai University.