Capturing User Preferences via Multi-Perspective Hypergraphs with Contrastive Learning for Next-Location Prediction
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...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7672 |
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| author | Fengyu Liu Kexin Zhang Chao Lian Yunong Tian |
| author_facet | Fengyu Liu Kexin Zhang Chao Lian Yunong Tian |
| author_sort | Fengyu Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aadfb6618cd74dbe89fbed73a3d09011 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-aadfb6618cd74dbe89fbed73a3d090112025-08-20T03:58:30ZengMDPI AGApplied Sciences2076-34172025-07-011514767210.3390/app15147672Capturing User Preferences via Multi-Perspective Hypergraphs with Contrastive Learning for Next-Location PredictionFengyu Liu0Kexin Zhang1Chao Lian2Yunong Tian3College of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaSchool of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaEngineering Laboratory of Industrial Vision Intelligent Equipment Technology, Chinese Academy of Sciences, Beijing 100190, ChinaWith 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.https://www.mdpi.com/2076-3417/15/14/7672location predictionhuman mobilitydisentangled representationhypergraph neural networkscontrastive learning |
| spellingShingle | Fengyu Liu Kexin Zhang Chao Lian Yunong Tian Capturing User Preferences via Multi-Perspective Hypergraphs with Contrastive Learning for Next-Location Prediction Applied Sciences location prediction human mobility disentangled representation hypergraph neural networks contrastive learning |
| title | Capturing User Preferences via Multi-Perspective Hypergraphs with Contrastive Learning for Next-Location Prediction |
| title_full | Capturing User Preferences via Multi-Perspective Hypergraphs with Contrastive Learning for Next-Location Prediction |
| title_fullStr | Capturing User Preferences via Multi-Perspective Hypergraphs with Contrastive Learning for Next-Location Prediction |
| title_full_unstemmed | Capturing User Preferences via Multi-Perspective Hypergraphs with Contrastive Learning for Next-Location Prediction |
| title_short | Capturing User Preferences via Multi-Perspective Hypergraphs with Contrastive Learning for Next-Location Prediction |
| title_sort | capturing user preferences via multi perspective hypergraphs with contrastive learning for next location prediction |
| topic | location prediction human mobility disentangled representation hypergraph neural networks contrastive learning |
| url | https://www.mdpi.com/2076-3417/15/14/7672 |
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