Point-of-interest (POI) recommendation has gained significant traction recently due to the rising trend of location-based networks. Traditional approaches rely on a centralized collection of user data. Concerning privacy protection, decentralized federated learning employs model training on each user’s device with nearby collaborative training techniques. However, existing decentralized federated recommendations suffer from two major problems: (1) Privacy risks: existing approaches expose geographical location or co-rated items information when constructing user neighborhoods. (2) Performance limitations: existing approaches adopt a simple model without incorporating auxiliary information. To solve these, we propose CA-PDBPR (category-aware privacy preserving POI recommendation using decentralized Bayesian personalized ranking) to address the above challenges. Specifically, we introduce a novel privacy-enhanced neighborhood creation method utilizing POI category preferences to calculate decentralized user similarity through secret sharing technology, ensuring a higher level of privacy. Moreover, we integrate POI category information with a refined Bayesian personalized ranking (BPR) loss function to enhance recommendation performance. Experimental evaluations conducted on real-world datasets validate the effectiveness of the CA-PDBPR model, demonstrating enhanced recommendation quality while minimizing data exposure compared with state-of-the-art alternatives.

由于基于位置的网络的崛起趋势，兴趣点 (POI) 推荐最近获得了巨大的关注。传统方法依赖于用户数据的集中收集。在隐私保护方面，去中心化联邦学习通过就近协作训练技术在每个用户的设备上进行模型训练。然而，现有的去中心化联合推荐存在两个主要问题：（1）隐私风险：现有方法在构建用户社区时暴露地理位置或共同评分的项目信息。 (2)性能限制：现有方法采用简单模型，没有结合辅助信息。为了解决这些问题，我们提出 CA-PDBPR（使用去中心化贝叶斯个性化排名的类别感知隐私保护 POI 推荐）来解决上述挑战。具体来说，我们引入了一种新颖的隐私增强邻域创建方法，利用 POI 类别偏好通过秘密共享技术计算分散的用户相似度，确保更高级别的隐私。此外，我们将 POI 类别信息与精细的贝叶斯个性化排名（BPR）损失函数相结合，以提高推荐性能。对现实世界数据集进行的实验评估验证了 CA-PDBPR 模型的有效性，证明了与最先进的替代方案相比，推荐质量得到了提高，同时最大限度地减少了数据暴露。