Privacy-preserving mobile application recommendation method based on trust fusion and filtering

Abstract

The invention belongs to the technical field of devices for executing special programs, and discloses a privacy-protected mobile application recommendation method based on trust fusion and filtering. Through multiple filtering and fusion of users' individual trust values ​​for mobile phone applications, data can be improved. Accurate and reliable, while providing accurate mobile application recommendations to users, it can also protect users' privacy and security. The generation of recommendation information is based on the fusion and filtering of real behavior data of users using mobile applications. The recommended results can reflect the real preferences of users to the greatest extent, with high personalization, and the accuracy of recommendation is greatly improved. In the process of data filtering and fusion, the security and reliability of the system are improved. The privacy data of the user can be protected sufficiently securely in the present invention; the scheme of protecting the privacy data of the user is adopted in the whole process, and at the same time, the accuracy and personalization of the recommendation results are ensured, and the security, reliability and operability of the system are guaranteed.

Description

technical field [0001] The invention belongs to the technical field of devices for executing special programs, and in particular relates to a method for recommending mobile applications with privacy protection based on trust fusion and filtering. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: Mobile smart phones are playing an increasingly important role in today's mobile Internet era, and mobile phones have become a vital tool for people's leisure and entertainment, work and life, and access to Internet services. The mobile application installed on the mobile phone is an essential interface for users to use the mobile phone. However, the large number of mixed applications in the mobile application market has brought users trouble in choosing. How to choose and install better mobile applications not only affects the application At the same time, it will also have an important impact on the user's own experie...

AI Technical Summary

Problems solved by technology

[0007] (4) Most of the existing recommendation methods for mobile applications lack the knowledge of the number of users when calculating recommendations for users. The data protection work is likely to cause the leakage of user privacy data and bring about privacy and security issues.

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