AI 中文总结
针对敏感用户数据云处理受隐私法规限制的问题，提出PriCoRec框架，通过云预排序加设备排序的协同结构，结合多样性正则化和云引导训练，实现隐私保护下的高效个性化广告推荐。
AI 中文摘要
隐私法规日益限制对敏感用户数据（如年龄、性别）的云处理，阻碍了传统仅基于云的推荐模型。为缓解这一挑战，我们提出了隐私感知云-设备协同广告推荐框架PriCoRec，该框架在将敏感特征保留在设备上的同时实现个性化推荐。将推荐拆分为基于云和基于设备的阶段虽能实现隐私感知部署，但由于私有特征有限，简单拆分会导致候选集质量下降和设备端推理效率低下。因此，我们设计了一个协同框架，包括使用云可访问特征的基于云的预排序阶段，以及本地结合高度个性化特征的设备端排序阶段。我们为预排序引入了多样性正则化器以提升候选质量。此外，为控制设备功耗和计算成本，我们采用了云引导训练机制，在保持模型轻量的同时提升设备模型性能。实验表明，所提框架在将敏感特征保留在设备上的同时，保持了强劲的推荐性能。
英文摘要
Privacy regulations increasingly restrict cloud processing of sensitive user data (e.g., age, gender), hindering traditional cloud-only recommendation models. To mitigate this challenge, we propose a Privacy-aware Collaborative cloud-device ads Recommendation framework (PriCoRec) which personalizes recommendations while keeping sensitive features on-device. While separating recommendation into cloud-based and on-device stages enables privacy-aware deployment, naive splitting suffers from degraded shortlist quality and inefficient on-device inference due to limited private features. We therefore design a collaborative framework that comprises a cloud-based pre-ranking stage using cloud-accessible features, and an on-device ranking stage that locally incorporates highly personalized features. We introduce a diversity regularizer to pre-ranking to improve candidate quality. Moreover, to control device power consumption and computational cost, we incorporate a cloud-guided training mechanism that enhances device model performance while keeping the model lightweight. Experiments demonstrate that the proposed framework maintains strong recommendation performance while keeping sensitive features on-device.
Comments5 pages, 1 figure. Accepted to RecSys'26
