A Federated Recommender System for Online Services
Ben Tan, Bo Liu, Vincent W. Zheng, Qiang Yang
Abstract
Due to privacy and security constraints, directly sharing user data between parties is undesired. Such decentralized data silo issues commonly exist in recommender systems. In general, recommender systems are data-driven. The more data it uses, the better performance it obtains. The data silo issues is a severe limitation of the recommender’s performance. Federated learning is an emerging technology, which bridges the data silos and builds machine learning models without compromising user privacy and data security. We design a recommender system based on federated learning. It is known as the federated recommender system. The system implements plenty of popular algorithms to support various online recommendation services. The algorithm implementation is open-sourced. We also deploy the system on a real-world content recommendation application, achieving significant performance improvement. In this demonstration, we present the architecture of the federated recommender system and give an online demo to show its detailed working procedures and results in content recommendations.