## 5 Experiments
### Ablation Study  
To comprehensively evaluate the effectiveness of each individual module within our proposed MPHCL framework, we conduct a detailed ablation study on two representative datasets--NYC and TKY--as illustrated in Figures 11 and 12. The full MPHCL model is denoted as the baseline, while \"w/o X\" indicates the model variant without component X. For each variant, we compare the performance using Recall@K and NDCG@K metrics. * **w/o \(\mathcal{G}_{\mathbb{C}}\)** represents removing the collective preference hypergraph. Figure 10: The impact of the embedding size \(d\) and layer number of the disentangled hypergraph representation learning network \(L\). (**a**) NYC dataset. (**b**) TKY dataset. * **w/o \(\mathcal{G}_{T}\)** represents removing the global transition flow graph. * **w/o \(\mathcal{G}_{C}\)** represents removing the geospatial context hypergraph. * **w/o CL** represents removing cross-view contrastive learning from the full MPHCL model. The key four findings are summarized as follows:  
1. On the NYC dataset, the removal of the collective preference hypergraph (\(\mathcal{G}_{C}\)) leads to the most pronounced decline in both Recall@K and NDCG@K scores. This indicates that capturing collaborative signals among users and items through high-order relations plays a central role in recommendation accuracy within dense urban environments like NYC, where user behaviors tend to be highly co-dependent. The contrastive learning module is the second most impactful, demonstrating that enforcing representational consistency and discrimination among hypergraph views significantly improves generalization. By contrast, the geospatial context hypergraph (\(\mathcal{G}_{C}\)) and global transition flow graph (\(\mathcal{G}_{T}\)) contribute to more modest gains. Notably, the global transition flow graph shows the least degradation when removed, implying that short-term behavioral transitions play a relatively minor role in this context, possibly due to the less sequentially structured urban mobility patterns of NYC users. 2. In the TKY dataset, the contrastive learning module emerges as the most crucial component, with its absence resulting in the most substantial drop in both performance metrics. This can be attributed to the relatively sparse and more heterogeneous nature of user behaviors in Tokyo, where enforcing consistency across hypergraph-structured  
Figure 11: Result of the ablation study on the NYC dataset. (**a**) Recall@K evaluation metric. (**b**) NDCG@K evaluation metric. Figure 12: Result of ablation study on the TKY dataset. (**a**) Recall@K evaluation metric. (**b**) NDCG@K evaluation metric. views becomes essential for learning robust representations. The collective preference hypergraph follows as the second most important component, reflecting the residual importance of collaborative user-item interactions. Interestingly, in contrast to NYC, the geospatial context hypergraph contributes more than the global transition flow graph, highlighting the stronger influence of location-based preferences in TKY's wider spatial layout. These results suggest that different urban characteristics and user interaction patterns lead to varying dependencies on spatial, sequential, and collaborative cues. 3. Across both datasets, a consistent pattern emerges in how the ranking cutoff \(K\) affects the relative importance of modules. At \(K=5\), where top-ranked predictions are emphasized, the global transition flow graph tends to be more influential than the geospatial context hypergraph, especially in the NYC dataset. This suggests that recent user behaviors and transition dynamics play a larger role in fine-grained recommendation scenarios. However, as \(K\) increases to 10, the Geospatial Context Hypergraph becomes more impactful. This shift can be explained by the fact that geographic cues, though more stable and less temporally sensitive, contribute to broader diversity in larger recommendation lists, while the directed nature of sequential transitions is more localized and immediate in its influence.