Matrix decomposition recommending method based on difference privacy protection

A technology of differential privacy and matrix decomposition, applied in digital data protection, instruments, complex mathematical operations, etc., can solve problems such as reduced data availability and large noise added to data sets

Inactive Publication Date: 2018-07-13
NANJING UNIV OF SCI & TECH
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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 ac

Method used

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  • Matrix decomposition recommending method based on difference privacy protection
  • Matrix decomposition recommending method based on difference privacy protection
  • Matrix decomposition recommending method based on difference privacy protection

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Embodiment 1

[0092] The matrix decomposition recommendation method based on differential privacy protection proposed by the present invention, the specific implementation process is as follows:

[0093] The core idea of ​​the collaborative filtering method is: by collecting the user's historical behavior data (evaluation information, purchase information, etc.), using the preferences of user groups with similar interests and similar behaviors to make personalized recommendations. In order to establish a recommendation model, the recommendation algorithm based on collaborative filtering needs to establish a certain relationship between the item and the user to achieve the recommendation, and the effect of the recommendation also depends on the establishment of the relationship between the item and the user. In the collaborative filtering algorithm, the user's preference for items is usually used as an n×m user-rating matrix R n×m To represent, n users use U={u 1 ,u 2 ,...,u n} means that...

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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...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06F21/62G06F17/16
CPCG06F17/16G06F21/6245G06F16/9535
Inventor 侯君李千目刘魁耿夏琛
Owner NANJING UNIV OF SCI & TECH
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