A vertical federation recommendation method based on clustering and latent factor model
Jianfei Zhang, Yuchen Jiang
Abstract
Recommendation systems have been widely used for user data mining. However, considering the security of user privacy and legal regulations, various institutions have begun to strengthen data privacy protection gradually. Federated learning is a novel joint training privacy protection framework, which can be executed without knowing the original data of each participant. In this paper, we propose a method based on clustering and latent factor model under the vertical federated recommendation system. Taking into account the diversity of a large number of different users in each participant and the complexity of the matrix factorization of the user-item matrix, we cluster the users to reduce the dimension of the matrix and improve the accuracy of user recommendations. In addition, in order to ensure the security of user data privacy, we use homomorphic encryption to protect data. We conducted experiments on the MovieLens movie dataset. Experiments show that effective clustering can significantly improve the accuracy of the recommendation system of each participant.