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Native ad is a popular type of online advertisement which has similar formswith the native content displayed on websites. Native ad CTR prediction isuseful for improving user experience and platform revenue. However, it ischallenging due to the lack of explicit user intent, and users' behaviors onthe platform with native ads may not be sufficient to infer their interest inads. Fortunately, user behaviors exist on many online platforms and they canprovide complementary information for user interest mining. Thus, leveragingmulti-platform user behaviors is useful for native ad CTR prediction. However,user behaviors are highly privacy-sensitive and the behavior data on differentplatforms cannot be directly aggregated due to user privacy concerns and dataprotection regulations like GDPR. Existing CTR prediction methods usuallyrequire centralized storage of user behavior data for user modeling and cannotbe directly applied to the CTR prediction task with multi-platform userbehaviors. In this paper, we propose a federated native ad CTR predictionmethod named FedCTR, which can learn user interest representations from theirbehaviors on multiple platforms in a privacy-preserving way. On each platform alocal user model is used to learn user embeddings from the local user behaviorson that platform. The local user embeddings from different platforms areuploaded to a server for aggregation, and the aggregated user embeddings aresent to the ad platform for CTR prediction. Besides, we apply LDP and DPtechniques to the local and aggregated user embeddings respectively for betterprivacy protection. Moreover, we propose a federated framework for modeltraining with distributed models and user behaviors. Extensive experiments onreal-world dataset show that FedCTR can effectively leverage multi-platformuser behaviors for native ad CTR prediction in a privacy-preserving manner.

FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior Data