HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation

2025-03-27

AI Summary

Existing POI recommendation methods struggle to model high-order heterogeneous relationships and inadequately address user behavior diversity and cold-start challenges—particularly for new users and new POIs. To tackle these issues, we propose a novel framework integrating heterogeneous hypergraphs with difficulty-aware meta-learning. Specifically, we construct a heterogeneous hypergraph incorporating three types of hyperedges: temporal behavioral patterns, spatial-functional correlations, and user preferences; and design a hypergraph neural network for joint spatiotemporal representation learning. Furthermore, we introduce a difficulty-aware diverse meta-learning mechanism to enable rapid, personalized adaptation of user representations. Extensive experiments on multiple real-world datasets demonstrate substantial improvements in recommendation accuracy—e.g., Recall@10 increases by 12.7%–23.4%—and significant mitigation of cold-start problems. Our approach establishes a new paradigm for modeling high-order relational structures and few-shot user representation learning in location-based services.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

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.

Problem

• Captures complex high-order relationships in POI recommendations
• Adapts to diverse user behaviors and cold-start issues
• Enhances recommendation accuracy with hypergraph and meta-learning

Innovation

• Hypergraph modeling captures high-order relationships
• Meta-learning adapts to diverse user behaviors
• Three hyperedges model temporal, spatial, preference