## 5 Experiments
### Baseline Model  
We compare the performance of our proposed Multi-Perspective Hypergraphs with Contrastive Learning (MPHCL) model with ten state-of-the-art baselines, which are popular for location prediction.  
* **NeuNext**[14] employs a spatio-temporal gated architecture that enhances traditional LSTM networks with time and distance gates to model user-location interaction sequences. It introduces two distinct pairs of time and distance gates to separately capture both short-term and long-term user interests.
* **LSTPM**[15] is designed to effectively capture and represent users' long-term preferences by utilizing a context-aware non-local network combined with a geo-nonlocal structure. It allows the model to account for the nuanced ways in which user interests evolve over time, taking into consideration various contextual factors. Furthermore, LSTPM incorporates a geo-dilated LSTM architecture to adeptly model users' short-term interests.
* **LightGCN**[42] presents a streamlined graph convolutional architecture specifically designed for collaborative filtering by removing non-essential processes, such as feature transformation and nonlinear activation.
* **EEDN**[43] utilizes a hybrid hypergraph convolution encoder designed to model interactions between users and locations. It integrates a matrix factorization decoder to facilitate effective feature alignment.
* **STAN**[16] employs a sophisticated bi-layer attention mechanism designed to capture the spatiotemporal correlations present in user trajectories, which allows the model to analyze and understand how users interact with different locations and items over time.  
* **GETNext**[17] introduces a comprehensive global trajectory flow map aimed at uncovering prevalent patterns in user movement. By leveraging GCNs, it transforms these attributes into latent embeddings, which facilitate a deeper understanding of user behavior dynamics.
* **CTRNext**[44] integrates a trajectory semantic similarity module alongside a multihead self-attention mechanism to capture collaborative signals derived from the check-in behaviors of similar users. This approach enables the model to analyze and understand the semantic relationships between user trajectories, thereby enhancing its ability to identify patterns and preferences shared among users with comparable behaviors.
* **MSTHN**[24] utilizes a multi-faceted spatial-temporal enhanced hypergraph network to capture intricate local interactions and high-order global collaborative signals. Furthermore, a user temporal preference augmentation module combines both local and global representations, improving the model's capacity to adjust to dynamic user behavior.
* **SLS-REC**[45] adopts a self-supervised graph neural architecture that explicitly models both long-term static and short-term dynamic user interests through a multi-view spatio-temporal learning strategy. The model constructs a Hawkes-process-based attention hypergraph to capture complex, high-order temporal and spatial dependencies for short-term interest modeling.
* **STHGCN**[25] leverages hypergraphs and aggregates multi-hop trajectory information to model the intricate relationships between user check-ins and their trajectories. This advanced framework integrates spatio-temporal data to provide a comprehensive understanding of user movement patterns.