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

TL;DR

FedCTR is a federated learning approach that leverages multi-platform user behavior data to improve native ad CTR prediction while preserving user privacy through local differential privacy techniques.

Contribution

This paper introduces FedCTR, a novel federated framework for native ad CTR prediction that effectively utilizes multi-platform user behaviors without compromising privacy.

Findings

FedCTR outperforms existing methods in CTR prediction accuracy.

The approach effectively preserves user privacy with LDP and DP techniques.

Extensive experiments validate the effectiveness of multi-platform data in CTR prediction.

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 challenging 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 privacy-sensitive 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. 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 LDP and DP techniques to the local and aggregated user embeddings respectively for better privacy protection. Moreover, we propose a federated framework for model training with distributed models and user behaviors. Extensive experiments on real-world dataset show that FedCTR can effectively leverage multi-platform user behaviors for native ad CTR prediction in a privacy-preserving manner.