HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910897618812928 |
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| author | Wang, Jinze Zhang, Tiehua Zhang, Lu Bai, Yang Li, Xin Jin, Jiong |
| author_facet | Wang, Jinze Zhang, Tiehua Zhang, Lu Bai, Yang Li, Xin Jin, Jiong |
| contents | Next Point-of-Interest (POI) recommendation aims to predict users' next locations by leveraging historical check-in sequences. Although existing methods have shown promising results, they often struggle to capture complex high-order relationships and effectively adapt to diverse user behaviors, particularly when addressing the cold-start issue. To address these challenges, we propose Hypergraph-enhanced Meta-learning Adaptive Network (HyperMAN), a novel framework that integrates heterogeneous hypergraph modeling with a difficulty-aware meta-learning mechanism for next POI recommendation. Specifically, three types of heterogeneous hyperedges are designed to capture high-order relationships: user visit behaviors at specific times (Temporal behavioral hyperedge), spatial correlations among POIs (spatial functional hyperedge), and user long-term preferences (user preference hyperedge). Furthermore, a diversity-aware meta-learning mechanism is introduced to dynamically adjust learning strategies, considering users behavioral diversity. Extensive experiments on real-world datasets demonstrate that HyperMAN achieves superior performance, effectively addressing cold start challenges and significantly enhancing recommendation accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22049 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation Wang, Jinze Zhang, Tiehua Zhang, Lu Bai, Yang Li, Xin Jin, Jiong Information Retrieval Social and Information Networks Next Point-of-Interest (POI) recommendation aims to predict users' next locations by leveraging historical check-in sequences. Although existing methods have shown promising results, they often struggle to capture complex high-order relationships and effectively adapt to diverse user behaviors, particularly when addressing the cold-start issue. To address these challenges, we propose Hypergraph-enhanced Meta-learning Adaptive Network (HyperMAN), a novel framework that integrates heterogeneous hypergraph modeling with a difficulty-aware meta-learning mechanism for next POI recommendation. Specifically, three types of heterogeneous hyperedges are designed to capture high-order relationships: user visit behaviors at specific times (Temporal behavioral hyperedge), spatial correlations among POIs (spatial functional hyperedge), and user long-term preferences (user preference hyperedge). Furthermore, a diversity-aware meta-learning mechanism is introduced to dynamically adjust learning strategies, considering users behavioral diversity. Extensive experiments on real-world datasets demonstrate that HyperMAN achieves superior performance, effectively addressing cold start challenges and significantly enhancing recommendation accuracy. |
| title | HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation |
| topic | Information Retrieval Social and Information Networks |
| url | https://arxiv.org/abs/2503.22049 |