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.

翻译：随着基于位置的社交网络（LBSNs）的快速发展，有效的下一兴趣点（POI）推荐技术的重要性日益凸显。该技术利用历史签到数据来预测用户接下来可能访问的兴趣点。传统的集中式深度神经网络（DNNs）虽然能提供出色的POI推荐性能，但面临着隐私问题和时效性受限的挑战。为此，基于设备的POI推荐方法被提出，其利用联邦学习（FL）和去中心化方法以确保隐私和推荐的时效性。然而，这些方法通常会给设备带来计算压力，并且难以适应新用户和新区域。本文提出了一种新颖的协同学习框架——基于扩散模型的云-边-端协同学习用于下一兴趣点推荐（DCPR），该框架利用了在多个领域已取得成功的扩散模型。DCPR采用云-边-端三层架构运行，以提供针对特定区域且高度个性化的POI推荐，同时减轻设备端的计算负担。DCPR通过独特的全局与局部学习过程相结合的方式，最大限度地降低了设备端的计算需求。我们在两个真实世界数据集上的评估表明，DCPR在推荐准确性、效率以及对新用户和新区域的适应性方面均表现出优越性能，标志着设备端POI推荐技术向前迈出了重要一步。