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

• URL: http://arxiv.org/abs/2608.14429v1
• Date: Fri, 14 Aug 2026 16:11:27 GMT
• Title: PriCoRec: A Privacy-Aware Cloud-Device Collaborative Framework for Ad Recommendation under Feature Constraints
• Abstract summary: We propose a Privacy-aware Collaborative cloud-device ads Recommendation framework (PriCoRec)<n>PriCoRec personalizes recommendations while keeping sensitive features on-device.<n>Experiments demonstrate that the proposed framework maintains strong recommendation performance while keeping sensitive features on-device.
• Score: 10.20038818942738
• License: http://creativecommons.org/licenses/by/4.0/
• 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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