## 4 Methodology
### Cross-View Contrastive Learning  
Contrastive learning [38; 39] is a prominent self-supervised learning approach that focuses on understanding the similarities and differences among data points. Its fundamental principle involves bringing similar sample pairs closer together in the embedding space while simultaneously pushing dissimilar pairs further apart. In this work, we propose a unified cross-view contrastive learning framework designed to enhance view-specific user and location prediction representations through self-supervised signals, effectively capturing the underlying cooperative associations among collective preference, sequential transition, and geospatial context perspectives.  
We incorporate a disentangled hypergraph representation learning module that generates distinct embeddings for each user and location prediction across the aforementioned views. These multi-view representations are subsequently aligned through a cross-view contrastive objective within an adaptive representation fusion module.  
Specifically, from the user perspective, we treat embeddings of the same user across different views as positive pairs, while embeddings from different users are regarded as negative pairs. To facilitate this alignment, we employ the InfoNCE loss function [40], which maximizes the agreement between the representations from different views. In the context of contrastive learning, inspired by [23], we explore the relationships among user representations derived from collaborative, transitional, and geographical perspectives. The following Equation (24) defines contrastive losses that help optimize our model by emphasizing similarities and dissimilarities among these different views. For instance, \(\mathcal{L}_{UI}\) represents the contrastive loss of the same user, \(u\), between collaborative and geospatial views.  
\[\mathcal{L}_{U}=\frac{1}{|U|}\sum_{u\in U}\left(-\log\frac{\exp(s(e_{C,u},e_{G, u})/\tau)}{\sum_{u^{\prime}\in U}\exp(s(e_{C,u^{\prime}},e_{G,u^{\prime}})/\tau)} \right), \tag{24}\]  
where \(e_{C,u}\in\mathbf{E}_{C,U}\), \(e_{G,u}\in\mathbf{E}_{G,U}\) represents user embeddings from different perspectives of user \(u\), \(s(\cdot,\cdot)\) is a cosine similarity function, and \(\tau\) is a temperature hyperparameters of the contrastive learning.  
From the location perspective, we replace user \(u\in U\) with location \(l\in L\) and define the contrastive losses, \(\mathcal{L}_{L}\), for the collaborative and geospatial views:  
\[\mathcal{L}_{L}=\frac{1}{|L|}\sum_{l\in L}\left(-\log\frac{\exp(s(e_{C,l},e_{G, l})/\tau)}{\sum_{l^{\prime}\in L}\exp(s(e_{C,l},e_{G,l^{\prime}})/\tau)}\right), \tag{25}\]  
To obtain the final self-supervised contrastive loss for the entire model, we weighted aggregate the contrastive loss from both user and location perspectives:  
\[\mathcal{L}_{CL}=\lambda_{UL}\odot\mathcal{L}_{U}+(1-\lambda_{UL})\odot \mathcal{L}_{L}, \tag{26}\]  
where \(\lambda_{UL}\) is a weighting coefficient to balance the contrastive loss between the user and location.  
This comprehensive self-supervised loss enhances the consistency and discriminative capability of multi-view representations. Moreover, to mitigate the overfitting issue and enhance robustness, we adopt a hypergraph augmentation strategy by applying hyperedge dropout on the constructed collective preference, sequential transition, and geospatial context hypergraphs. This dropout, controlled by a ratio \(d_{p}\), introduces structural perturbations and promotes generalizable representation learning under noisy conditions.