###### Abstract
Despite the substantial advancements in location prediction, three critical challenges remain unresolved, limiting the effectiveness of current models:  
First, a prevalent limitation lies in the entanglement of heterogeneous behavioral signals--such as collective user preferences, sequential transition patterns, and geographical proximity--into a unified latent representation. This monolithic embedding often obscures the distinct contribution of each factor, making it difficult to interpret the underlying decision-making process and reducing the model's ability to adapt to varying user behaviors across contexts. Second, although some recent approaches [27, 28, 29] leverage graph-based or multi-relational structures to capture diverse interaction patterns, they frequently fall short in terms of explicit disentanglement. Instead of modeling each behavioral perspective independently, these models often integrate multiple views into a single graph structure or latent space without clearly separating their semantic roles. As a result, the learned  
Figure 1: An illustration of the next-location prediction. representations tend to be entangled and coarse-grained, which hampers the model's ability to capture fine-grained, view-specific dynamics essential for accurate location prediction. Third, even in models [30; 31] that do acknowledge multiple views, there is a notable lack of mechanisms for explicit alignment or contrastive integration across these perspectives. Without encouraging consistency or highlighting distinctions among views, the model fails to fully exploit the complementary nature of multi-perspective information. This deficiency becomes especially pronounced in scenarios characterized by data sparsity and noisy check-in records, where robust and discriminative representations are crucial for generalization. Consequently, the inability to systematically disentangle and reconcile different behavioral signals remains a bottleneck in achieving accurate location predictions. To address the above challenges in location prediction, we propose a novel framework named Multi-Perspective Hypergraphs with Contrastive Learning (MPHCL), which captures and disentangles user preferences by integrating three key behavioral dimensions. The framework comprises three individual graphs: (1) the collective preference hypergraph for collaborative preferences within the user community, (2) the geospatial context hypergraph for spatial correlations between locations, and (3) the global transition flow graph for mobility patterns based on visit sequences. This multi-perspective approach enables nuanced disentanglement of behavioral factors influencing user mobility. We introduce a unified hypergraph representation learning network that ensures the independence of each perspective during embedding, utilizing a node-hyperedge-node message passing framework and across-hyperedge propagation to prevent feature entanglement. Additionally, a cross-view contrastive learning mechanism aligns multi-view representations by treating embeddings of the same user or location across different views as positive pairs while considering others as negative. This approach enhances consistency, strengthens the robustness of learned embeddings, especially under sparse or noisy data, and facilitates effective integration of diverse signals. Consequently, MPHCL provides a comprehensive and personalized understanding of user intent, leading to improved performance in location prediction tasks. In short, our main contributions are summarized as follows:  
* We propose a novel Multi-Perspective Hypergraphs with Contrastive Learning (MPHCL) model for location prediction, which disentangles user preferences by leveraging hypergraphs from three perspectives. Our unified hypergraph representation learning network incorporates a two-step information propagation scheme to capture high-order locations, including within-hyperedge feature aggregation and across-hyperedge feature propagation.