Matrix decomposition recommending method based on difference privacy protection

Abstract

The invention discloses a matrix decomposition recommending method based on difference privacy protection. The method comprises the steps of converting the collected evaluation or preference of usersfor an object into a user-score matrix as a training set of a recommending method model; utilizing a score average value, a user factor matrix, an object factor matrix, a user bias item and an objectbias item to predict score conditions for the object by the users; through a difference privacy average value calculation method, calculating an average value of the user scores under difference privacy protection; according to a score prediction model, building a minimum square error function; through a difference privacy random gradient lowering method, training the score prediction model, adding difference privacy noise in the training process and achieving the difference privacy protection of parameters; through the score prediction model and trained difference privacy protection model parameters, predicting scores for the object by the users. When a recommending result is provided, information of the users can be subjected to the difference privacy protection, and the recommending accuracy is high.

Description

technical field [0001] The invention relates to the technical field of data analysis and data mining, in particular to a matrix decomposition 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 networking, e-commerce and electronic payment, etc. People's daily Work and life are inseparable from the Internet and mobile Internet. With the rapid growth of the number of netizens and the number of website applications, all kinds of information on the Internet are also growing rapidly. With the huge base of netizens and websites, the amount of information increasing every moment has exceeded the capacity of ordinary people. This makes it impossible for people to actively and effectively fin...

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 the problem of adding too much noise to the data set and reducing data availability.

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