Asthn
Adaptive Spatio-Temporal Hypergraph Network for Next POI Recommendation
Bibliographic Data
| ID | 22034857 |
|---|---|
| Authors | Fang Liu (0000-0002-7132-354X, Shenyang Ligong University), Tianrui Li (0000-0001-7780-104X, Shenyang Ligong University), Jiangtao Li (0000-0003-2995-4759, Shenyang Ligong University) |
| Year | 2026 |
| Volume | 15 |
| Issue | 6 |
| Pages | 242 |
| Publication date | 2026-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi15060242 |
| OpenAlex | W7163054891 |
| Language | EN |
| References cited | 26 |
The widespread use of mobile Internet- and location-based services has generated large-scale check-in data in location-based social networks, creating opportunities for intelligent urban-mobility analysis and personalized mobility services. Making the next point-of-interest (POI) recommendation is an important task in this setting because it supports context-aware destination suggestion, travel assistance, and smart mobility services. However, existing methods still face challenges in jointly modeling higher-order mobility patterns, uneven time intervals, geographic reachability, and fine-grained intra-day temporal regularities. To address these issues, this paper proposes ASTHN, an Adaptive Spatio-Temporal Hypergraph Network for next POI recommendation. ASTHN constructs three fine-grained spatio-temporal context hypergraphs from minimum time interval, spatial proximity, and hourly preference, and uses hypergraph neural networks to learn view-specific POI representations. A context-adaptive fusion module then aligns and integrates multi-source spatio-temporal signals, while an ST-GRU with spatio-temporal gates captures dynamic trajectory evolution. Temperature scaling is further applied at the output layer to alleviate overly concentrated score distributions. Experiments on Foursquare-NYC and Foursquare-TKY show that ASTHN consistently outperforms representative baselines. With results reported as mean ± std over three random seeds, ASTHN improves over the strongest baseline by 3.79%, 14.62%, 2.28%, and 1.24% on NYC in Recall@5, Recall@10, NDCG@5, and NDCG@10, respectively. On TKY, the corresponding improvements are 5.83%, 37.20%, 13.86%, and 20.49%. Ablation, parameter, complexity, and application-oriented case analyses further demonstrate the effectiveness, stability, and practical usability of ASTHN for next POI recommendation in urban-mobility scenarios
Artificial neural network · Hypergraph · Trajectory · Usability · Human Mobility and Location-Based Analysis · Traffic Prediction and Management Techniques · Transportation and Mobility Innovations
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| Citation velocity | historical |
|---|---|
| Highly cited | No |