As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and interesting places. Currently, most POI recommenders are based on the conventional centralized paradigm that heavily relies on the cloud to train the recommendation models with large volumes of collected users' sensitive check-in data. Although a few recent works have explored on-device frameworks for resilient and privacy-preserving POI recommendations, they invariably hold the assumption of model homogeneity for parameters/gradients aggregation and collaboration. However, users' mobile devices in the real world have various hardware configurations (e.g., compute resources), leading to heterogeneous on-device models with different architectures and sizes. In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e.g., dimension \& number of hidden layers). To counteract the sparsity of on-device user data, we propose to pre-select neighbors for collaboration based on physical distances, category-level preferences, and social networks. To assimilate knowledge from the above-selected neighbors in an efficient and secure way, we adopt the knowledge distillation framework with mutual information maximization. Instead of sharing sensitive models/gradients, clients in MAC only share their soft decisions on a preloaded reference dataset. To filter out low-quality neighbors, we propose two sampling strategies, performance-triggered sampling and similarity-based sampling, to speed up the training process and obtain optimal recommenders. In addition, we design two novel approaches to generate more effective reference datasets while protecting users' privacy.

翻译：作为基于位置社交网络（LBSNs）中不可或缺的个性化服务，下一个兴趣点（POI）推荐旨在帮助用户发现具有吸引力和趣味性的地点。当前多数POI推荐器采用传统集中式范式，严重依赖云端通过收集大量用户敏感签到数据来训练推荐模型。尽管近期有少数研究探索了面向设备的弹性隐私保护POI推荐框架，但这些工作无一例外地假设模型同质性以进行参数/梯度聚合与协作。然而，现实世界中用户的移动设备具有不同的硬件配置（如计算资源），导致异构设备模型在架构和规模上存在差异。鉴于此，我们提出了一种新颖的面向设备POI推荐框架——模型无关的协作学习（MAC），允许用户自定义模型结构（例如隐藏层维度与数目）。为应对设备用户数据的稀疏性，我们提出基于物理距离、类别偏好和社交网络的邻居预选协作机制。为了高效安全地吸收所选邻居的知识，我们采用基于互信息最大化的知识蒸馏框架。与共享敏感模型/梯度不同，MAC中的客户端仅共享其在预加载参考数据集上的软决策。为过滤低质量邻居，我们提出两种采样策略——性能触发采样和基于相似性的采样，以加速训练过程并获得最优推荐器。此外，我们设计了两种新颖方法，在保护用户隐私的同时生成更有效的参考数据集。