在保护用户隐私的前提下，实现云端与设备端协同广告推荐。
PriCoRec: A Privacy-Aware Cloud-Device Collaborative Framework for Ad Recommendation under Feature Constraints
- 云端预排期用公开特征，设备端本地排序融合私密特征。
- 引入多样性正则化提升候选集质量，避免推荐结果单一。
- 云指导训练让设备模型轻量高效，适合移动终端部署。
隐私法规日益限制对敏感用户数据（如年龄、性别）的云端处理，阻碍了传统纯云端推荐模型的应用。为此，我们提出一种隐私感知的云-设备协同广告推荐框架PriCoRec，实现个性化推荐的同时将敏感特征保留在设备端。通过将推荐过程分为云端预排序和设备端排序两阶段，既保障隐私又提升效率。为解决简单分割导致候选集质量下降及设备端推理效率低的问题，我们设计了包含多样性正则化的预排序机制，以改善候选质量；同时引入云引导训练策略，在保持设备模型轻量化的同时提升其性能。实验表明，该框架在保护敏感特征不外泄的前提下，仍能维持较强的推荐效果。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。