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arXiv · 2608.14429

PriCoRec: A Privacy-Aware Cloud-Device Collaborative Framework for Ad Recommendation under Feature Constraints

Dairui Liu·Zhongyi Lu·Jitao Lu·Aghiles Salah·Mete Sertkan·Roger Zhe Li·Changhong Jin·Barry Smyth·Xingsheng Guo·Ruihai Dong
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Abstract

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.

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Dairui Liu, Zhongyi Lu, Jitao Lu, Aghiles Salah, Mete Sertkan, Roger Zhe Li, Changhong Jin, Barry Smyth, Xingsheng Guo, Ruihai Dong. 2026-08-14. PriCoRec: A Privacy-Aware Cloud-Device Collaborative Framework for Ad Recommendation under Feature Constraints. https://doi.org/10.1145/3773078.3831838

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