Abstract
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.
Liu Yang and Ben Tan are both co-first authors with equal contribution. This work was done during Liu Yang’s internship at WeBank.
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- 1.
GDPR is a regulation in EU law on data protection and privacy in the European Union and the European Economic Area. https://gdpr.eu/.
- 2.
5G is the fifth generation wireless technology for digital cellular networks.
- 3.
A blockchain is a growing list of records, called blocks, that are linked using cryptography.
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Yang, L., Tan, B., Zheng, V.W., Chen, K., Yang, Q. (2020). Federated Recommendation Systems. In: Yang, Q., Fan, L., Yu, H. (eds) Federated Learning. Lecture Notes in Computer Science(), vol 12500. Springer, Cham. https://doi.org/10.1007/978-3-030-63076-8_16
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