Horizontal Cross-Silo Federated Recommender Systems

Abstract

Recommender systems (RSs) completely rely on the knowledge of training information to generate recommendations. However, due to privacy, ownership, and protection of users’ information, such training information is not easily accessible or shared with an RS. Moreover, with recent regulations in privacy laws (e.g, GDPR), collecting user preferences and perform centralized training may not be feasible. Federated Learning (FL) is a form of machine learning technique where the goal is to learn a high-quality recommendation model without never directly accessing raw training data. In this work, we specifically focus on situations where multiple stakeholders (referred to as corporate companies like e-commerce business partners, hospitals, banks, news media publishers) participate in federated learning to build a shared recommendation model. We performed offline experiments by simulating a real federated learning setup and investigated the benefits federated learning brings to stakeholders in terms of ranking compared to an RS model trained without participating in federated learning. Our experimental results reveal that stakeholders can significantly benefit from federated learning to generate accurate recommendations. Moreover, we also study the use and benefits of federated learning in situations when there are not enough preferences available for users.

Topics & Concepts