# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 5 Experiment
### Ablation Experiment  
The model proposed in this paper can be seen as a synthesis of three components: heterogeneous graph contrastive learning (HGCL), graph structure learning (GSL), and sequence encoding (SE). To demonstrate the effectiveness of each component, this paper demonstrates the models' effectiveness and some parts of these components. Specifically, we have designed six variants based on the proposed model:  
1. w/o HGCL: This variant means that, when learning the representation vectors for users and POIs, the heterogeneous graph contrastive learning part is removed, and the encoding results of different meta-path subgraphs are directly used without counting the contrastive loss.
2. w/o InterCL: This variant means removing the second kind of contrastive learning method, the interaction-aware contrastive learning. This means, when we use contrastive learning, we do not consider the interaction graph; thus, the \(L_{CL2}\) is not calculated in the main loss.
3. w/o 1 MP: This variant means we only use one meta-path for one type of node. Without another meta-path, the contrastive loss is also removed.
4. w/o GSL: It means that the graph structure learning part is removed, and the fusion of meta-path subgraph coding results is taken as the final representation of users and POIs.
5. w/o bi-att: It means that the bidirectional attention in the sequence model is removed and only a normal GAT layer is used as an alternative.
6. w/o extrInfo: This variant removes the attention mechanism, extra-temporal interval, and geographical distances. We only use a plain Graph Convolution Neural Network (GCN) to learn the users' sequence preferences.  
The results in Table 7 show that different components of the model have varying effects, and removing any component has reduced the models' performance to some extent. Specifically, removing the heterogeneous graph contrastive learning component had a minimal impact, indicating that the model can still make accurate predictions to some extent. Moreover, the interaction-aware contrastive learning is also important, suggesting the two kinds of contrastive learning methods can capture the relations from different perspectives. The meta-paths can also enhance the performance from different relations. The graph structural learning can help refine the graph structure, indirectly influencing the final results. Removing this component can also impact the recommendation performance. The different parts in the sequence modeling have the most influence: Removing the bidirectional attention mechanism from the sequence encoding leads to a more significant decline in performance, while removing the extra information drops the highest amount of  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Datasets** & **Training Time (Per Epoch)** & **Testing Time** \\ \hline NYC & 4 min 8 s & 2.97 s \\ \hline TKY & 56 min 20 s & 9.41 s \\ \hline CA & 39 min 27 s & 17.56 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time consuming for training and testing on different datasets.  
performance in all datasets. They suggest the attention mechanism and extra information are effective in capturing the sequence and better understanding the relations between POIs, thus more accurately reflecting users' sequence preferences.