Abstract: Recommendation system mainly provide personalized services based on user information.However,users are widely concerned about data privacy issues,which poses new challenges to current recommendation algorithms.Existing works mainly address this problem based on the perspectives of differential privacy,anonymization,cryptography,and federated learning.Data disturbance and computational complexity are the main shortcomings of existing methods.Different from existing work,this paper proposes a collaborative filtering model based on curriculum learning and graph neural network(CLG-CF),which makes full use of rating information to learn the embedding of user and item in implicit feedback scenarios.CLG-CF utilizes a bipartite graph modeling scoring table,then realizes the representation learning of users and items based on graph convolutional networks,finally completes the prediction of(user,item) pairs through a multi-layer neural network.During the training process of the CLG-CF model,negative sampling is used to enhance training samples.In order to solve the problem of samples’ authenticity,the curriculum learning is innovatively introduced to guide the model learning.Extensive experiments are conducted on three real large-scale datasets,and the results show that the CLG-CF model can achieve good recommendation results without using user and item information.

Key words: Recommendation system, Collaborative filtering, Curriculum learning, Graph embedding, Data privacy