# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 5 Experiment
### Experiment Results
#### 5.2.4 Further Analysis of Heterogeneous Graph Representation Learning  
The representation learning section of this paper primarily focuses on heterogeneous graph contrastive learning and graph structure learning. In this part, a heterogeneous graph is firstly constructed based on the provided information, like users, POIs, and POI attributes. Due to lack of users' side information, like the friendship among users, we use different meta-paths to derive meta-path-based subgraphs for users and POIs. In this section, the experiments are conducted on the CA dataset as it provides the user friendship information, which can help construct users' contrastive views and validate the effectiveness of our proposed model. Specifically, we set the following experiments:  
1. Real relations: When learning the representation vectors of users and POIs, the meta-path-based subgraphs are no longer used for learning. We use users' interpersonal friendship in the CA dataset to construct users' side graph for contrastive learning. If two users are friends, they are connected in this graph and the corresponding value is set to 1 in the adjacency matrix. For the POI relation graph, the actual distance is used to construct the edges. If the distance between two POIs is less than a threshold, i.e., 1 km, these POIs are connected. 2. Full structure: This set uses the full structure of the proposed model in this article. 3. Random graph: This set replaces the meta-path-based subgraphs with randomly constructed graphs. The random graphs have the same number of nodes and edges with the meta-path-based subgraphs, but each edge is constructed by randomly selecting two nodes. 4. A subgraph: When learning the representations of users and POIs, this set only uses one meta-path-based subgraph for each type of node. 5. Two-stage training: This set separately trains the model in two stages. The heterogeneous graph contrastive learning part is first trained until the convergence of the contrastive loss. Then, the remaining components are trained with the parameters of heterogeneous graph contrastive learning frozen. Two-stage training can help improve the efficiency of training and alleviate the computation overload, but may reduce the coherence between different stages. The experimental results are shown in Table 5. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Experimental Setup** & **Acc@1** & **Acc@5** & **Acc@10** & **MRR** \\ \hline Real relations & 0.1794 & 0.3677 & 0.4302 & 0.2614 \\ \hline Full structure & 0.1788 & 0.3651 & 0.4258 & 0.2602 \\ \hline Random graph & 0.1586 & 0.3241 & 0.3632 & 0.2403 \\ \hline A subgraph & 0.1720 & 0.3547 & 0.4210 & 0.2589 \\ \hline Two-stage training & 0.1400 & 0.3111 & 0.3726 & 0.2204 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Further analysis results. The results in Table 4 show that the model designed in this paper closely matches the recommendation outcomes from real datasets, with a difference of less than 1%, demonstrating that the model can mitigate the impact of missing data to some extent. Additionally, the study compares different numbers of meta-path-based subgraphs, revealing that different meta-path-based subgraphs can provide different semantic information, thereby influencing the recommendation performance. The results on random graphs also dropped about 11.30% and 7.65% in Acc@1 and MRR, respectively, indicating the meta-path-based subgraphs can capture semantic information. Finally, a two-stage training experiment aims to reduce the required computational resource. However, the experimental results indicate that this method did not yield satisfactory outcomes, possibly due to the representation learning module not aligning well with the downstream sequence modeling when separately training. It should be noticed that the structural differences are inescapable, as our goal is not to substitute real graphs, but to leverage domain knowledge, like meta-paths, to extract task-relevant semantics that may be missing or noisy in the observed relations.