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

TL;DR

Hypergraph-enhanced Meta-learning Adaptive Network (HyperMAN) is proposed, a novel framework that integrates heterogeneous hypergraph modeling with a difficulty-aware meta-learning mechanism for next POI recommendation, effectively addressing cold start challenges and significantly enhancing recommendation accuracy.

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.

Figures (6)

• Figure 1: Comparison between (a) a hypergraph and (b) a heterogeneous hypergraph.
• Figure 2: Comparison behavioral diversity.
• Figure 3: The overall framework of our proposed HyperMAN.
• Figure 4: Performance comparison for variants of HyperMAN on the four datasets.
• Figure 5: Sensitivity analysis for Inner Loop results.