###### Abstract  
With the widespread adoption of mobile devices and the increasing availability of user trajectory data, accurately predicting the next location a user will visit has become a pivotal task in location-based services. Despite recent progress, existing methods often fail to effectively disentangle the diverse and entangled behavioral signals, such as collaborative user preferences, global transition mobility patterns, and geographical influences, embedded in user trajectories. To address these challenges, we propose a novel framework named Multi-Perspective Hypergraphs with Contrastive Learning (MPHCL), which explicitly captures and disentangles user preferences from three complementary perspectives. Specifically, MPHCL constructs a global transition flow graph and two specialized hypergraphs: a collective preference hypergraph to model collaborative check-in behavior and a geospatial-context hypergraph to reflect geographical proximity relationships. A unified hypergraph representation learning network is developed to preserve semantic independence across views through a dual propagation mechanism. Furthermore, we introduce a cross-view contrastive learning strategy that aligns multi-perspective embeddings by maximizing agreement between corresponding user and location representations across views while enhancing discriminability through negative sampling. Extensive experiments conducted on two real-world datasets demonstrate that MPHCL consistently outperforms state-of-the-art baselines. These results validate the effectiveness of our multi-perspective learning paradigm for next-location prediction. location prediction; human mobility; disentangled representation; hypergraph neural networks; contrastive learning 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., 2020, pp. 1-10. IEEE. Personal notice on this material is permitted. Permission notice on this material is permitted. in Figure 1. It aims to infer user preferences and contextual patterns from historical trajectory data and predict which location predictions users are most likely to visit next. Location prediction addresses the fundamental problem: leveraging users' spatiotemporal behavioral history to anticipate their next geographical action. Numerous models have been developed to address this task, which can broadly be categorized into four main groups. The first group comprises traditional approaches such as collaborative filtering (CF) [8,9] and matrix factorization (MF) [10,11], which capture co-visitation or preference patterns but often struggle with sequential or spatial dynamics. The second category includes recurrent neural network (RNN)-based models [12, 13, 14, 15], which utilize temporal sequence modeling to learn the evolution of user preferences over time. However, these models often face limitations in capturing long-term dependencies and complex spatial interactions. The third group introduces attention-based models [16, 17, 18, 19] that dynamically assign weights to different historical check-ins or contextual factors, improving the ability to model user intent. Finally, graph-based [20, 21, 22] and hypergraph-based methods [23, 24, 25] have emerged as powerful tools for modeling high-order and non-Euclidean user-location prediction relationships by representing check-in sequences and spatial clusters as graphs or hypergraphs. These approaches offer enhanced flexibility in representing multifaceted interactions but still face challenges in disentangling diverse behavioral influences effectively. In recent studies, contrastive learning has also been coupled with advanced probabilistic methods such as Variational Bayes [26]. These methods provide a robust framework for sparse recovery, enabling more efficient learning in high-dimensional spaces.