Research On News Recommendation Algorithm Based On Differential Privacy

In the era of big data,news information is seriously overloaded,so the application of news recommendation system is very necessary.However,the user attributes,behaviors and other data obtained by the news recommendation system contain a large amount of user privacy information,which can easily lead to privacy leakage.Therefore,research on news recommendation algorithm based on differential privacy is an important work.Aiming at the problem of privacy leakage in news recommendation algorithm,this pa-per designs a privacy protection model based on the differential privacy mechanism and NRMS model.In the user coding module of the model,the privacy budget is allocated adaptively according to the weights in the self-attention mechanism,so as to better pro-tect user privacy and ensure data availability.Further,it is theoretically proved that the proposed privacy-preserving algorithm satisfies the definition of differential privacy,and the model designed in this paper is verified to be superior in protecting user privacy and ensuring data availability on real news datasets.