Privacy-preserving graph convolution network for federated item recommendation

Abstract

In traditional recommender systems, we often build models based on a centralized storage of user data, which however will lead to user privacy concerns and risks. In this paper, we study an emerging and important recommendation problem called federated item recommendation (FIR), in which a recommendation model is built with decentralized data of user-item interactions in a privacy-aware manner, i.e., the personal behavior data of each user does not leave the owner. Recently, graph neural network (GNN) has been widely recognized as a state-of-the-art solution for item recommendation with implicit feedback since it is able to model the high-order connectivity between users and items. However, it is very challenging to exploit the high-order connectivity information in a decentralized user-item interaction graph without compromising user privacy. To address that, we propose a GNN-based federated recommendation framework, i.e., privacy-preserving graph convolution network (P-GCN), for the studied problem of FIR. Our P-GCN can leverage the high-order connectivity information like a centralized GCN model such as LightGCN, though it is built using a decentralized user-item graph. To achieve that, we design a novel privacy-preserving graph convolution approach based on secure aggregation and employ item-based user representation to compensate for the performance loss it causes due to the protection of user privacy. Moreover, we improve a group-wise concealing strategy for protecting user privacy. Empirical studies on three datasets show that our P-GCN can achieve similar or even better performance comparing with the non-federated (i.e., centralized) counterpart, and outperforms all the existing federated methods for the studied problem.