Federated Recommendation Systems

Recommender systems are heavily data-driven. In general, the more data the recommender systems use, the better the recommendation results are. However, due to privacy and security constraints, directly sharing user data is undesired. Such decentralized silo issues commonly exist in recommender systems. There have been many pilot studies on protecting data privacy and security when utilizing data silos. But, most works still need the users’ private data to leave the local data repository. Federated learning is an emerging technology, which tries to bridge the data silos and build machine learning models without compromising user privacy and data security. In this chapter, we introduce a new notion of federated recommender systems, which is an instantiation of federated learning on decentralized recommendation. We formally define the problem of the federated recommender systems. Then, we focus on categorizing and reviewing the current approaches from the perspective of the federated learning. Finally, we put forward several promising future research challenges and directions.