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

Abstract

Native ad is a popular type of online advertisement which has similar forms with the native content displayed on websites. Native ad CTR prediction is useful for improving user experience and platform revenue. However, it is a challenging task due to the lack of explicit user intent, and users' behaviors on the platform with native ads may not be sufficient to infer their interest in ads. Fortunately, user behaviors exist on many online platforms and they can provide complementary information for user interest mining. Thus, leveraging multi-platform user behaviors is useful for native ad CTR prediction. However, user behaviors are highly privacysensitive and the behavior data on different platforms cannot be directly aggregated due to user privacy concerns and data protection regulations like GDPR. Existing CTR prediction methods usually require centralized storage of user behavior data for user modeling and cannot be directly applied to the CTR prediction task with multi-platform user behaviors. In this paper, we propose a federated native ad CTR prediction method named FedCTR, which can learn user interest representations from their behaviors on multiple platforms in a privacy-preserving way without the need of centralized storage. On each platform a local user model is used to learn user embeddings from the local user behaviors on that platform. The local user embeddings from different platforms are uploaded to a server for aggregation, and the aggregated user embeddings are sent to the ad platform for CTR prediction. Besides, we apply local differential privacy (LDP) and differential privacy (DP) techniques to the local and aggregated user embeddings respectively for better privacy protection. Moreover, we propose a federated framework for collaborative model training with distributed models and distributed user behaviors. Extensive experiments on real-world dataset demonstrate that the proposed method can effectively leverage multi-platform user behaviors for native ad CTR prediction in a privacy-preserving manner.