  The rapid expansion of Location-Based Social Networks (LBSNs) has highlighted
the importance of effective next Point-of-Interest (POI) recommendations, which
leverage historical check-in data to predict users' next POIs to visit.
Traditional centralized deep neural networks (DNNs) offer impressive POI
recommendation performance but face challenges due to privacy concerns and
limited timeliness. In response, on-device POI recommendations have been
introduced, utilizing federated learning (FL) and decentralized approaches to
ensure privacy and recommendation timeliness. However, these methods often
suffer from computational strain on devices and struggle to adapt to new users
and regions. This paper introduces a novel collaborative learning framework,
Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI
Recommendations (DCPR), leveraging the diffusion model known for its success
across various domains. DCPR operates with a cloud-edge-device architecture to
offer region-specific and highly personalized POI recommendations while
reducing on-device computational burdens. DCPR minimizes on-device
computational demands through a unique blend of global and local learning
processes. Our evaluation with two real-world datasets demonstrates DCPR's
superior performance in recommendation accuracy, efficiency, and adaptability
to new users and regions, marking a significant step forward in on-device POI
recommendation technology.
