ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit

As an emerging privacy-preserving approach to leveraging crossplatform user interactions, vertical federated learning (VFL) has been increasingly applied in recommender systems. However, vanilla VFL is only applicable to overlapped users, ignoring potential universal interest patterns hidden among non-overlapped users and suffers from limited user group benefits, which hinders its application in real-world recommenders.

In this paper, we extend the traditional vertical federated recommendation problem (VFR) to a more realistic Fully-Vertical federated recommendation setting (Fully-VFR) which aims to utilize all available data and serve full user groups. To tackle challenges in implementing Fully-VFR, we propose a Retrieval-enhanced Vertical Federated recommender (ReFer ), a groundbreaking initiative that explores retrieval-enhanced machine learning approaches in VFL. Specifically, we establish a general "retrieval-and-utilization" algorithm to enhance the quality of representations across all parties. We design a flexible federated retrieval augmentation (RA) mechanism for VFL: (i) Cross-RA to complement field missing and (ii) Local-RA to promote mutual understanding between user groups. We conduct extensive experiments on both public and industry datasets. Results on both sequential and non-sequential CTR prediction tasks demonstrate that our method achieves significant performance improvements over baselines and is beneficial for all user groups.