Neighborhood recommendation method based on differential privacy protection

Abstract

The invention discloses a neighborhood recommendation method based on differential privacy protection. The neighborhood recommendation method comprises the steps that firstly, collected article evaluations or preferences of users are converted into a user-scoring matrix serving as a training set of a recommendation method model at a training stage; secondly, a scoring prediction model is established by utilizing a neighborhood-based recommendation method, and the article scoring situations of the users are predicted. In the neighborhood-based recommendation method, and an average value under the differential privacy protection, user bias items and article bias items are calculated; at a scoring prediction stage, a differential privacy protection method based on an index mechanism is utilized to select neighbors; the local sensitivity of similarity is utilized to add Lapras noise for differential privacy protection; finally, the scoring prediction model and trained differential privacyprotection model parameters are utilized to predict articles scores of the users. By adopting the neighborhood recommendation method, differential privacy protection can be conducted on the information of the users when recommendation results are provided, and the method has higher recommendation accuracy.

Description

Technical field [0001] The invention relates to the technical field of data analysis and data mining, in particular to a neighborhood recommendation method based on differential privacy protection. Background technique [0002] In today’s society, with the rapid popularization and development of the Internet and mobile Internet, various network applications and mobile apps have been integrated into all aspects of people’s daily work and life, such as instant messaging, social networks, e-commerce, and electronic payments. Work and life are inseparable from the Internet and mobile Internet. While the number of netizens and the number of website applications is growing rapidly, all kinds of information on the Internet are also accelerating. With the huge base of netizens and websites, the amount of information added every moment has exceeded the capacity of ordinary people. This makes it impossible for people to actively and effectively find, process, and use the data they want in...

AI Technical Summary

Problems solved by technology

Due to the fact that differential privacy protection is mostly implemented by adding noise to the data set or the output of the method in the actual use process, if it is not used properly, it will cause excessive noise in the data set and reduce data availability.

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