Item recommendation method and device for protecting user privacy and learning system

Abstract

An item recommendation method, a device and a learning system for protecting user privacy are provided. The method includes: obtaining a locally collected scoring matrix, the scoring matrix being used to describe a scoring situation of multiple users on multiple items; locally training an item expression matrix and a user expression matrix corresponding to the scoring matrix, and sending a first user expression matrix obtained by local training to a server, so that the server aggregates and processes user expression matrices independently uploaded by the multiple data devices, and sends an aggregated second user expression matrix to the multiple data devices; and predicting degree of preferences of the multiple users for the multiple items according to the item expression matrix obtained by the local training and the second user expression matrix, so that item recommendations are performed for the multiple users according to the degrees of preference.