Mobile application recommendation method capable of privacy protection based on trust fusion and filtering

Abstract

The invention belongs to the technical field of devices for executing special programs, and discloses a mobile application recommendation method capable of privacy protection based on trust fusion andfiltering, which improves the accuracy and reliability of data by filtering and merging individual trust values of users for mobile phone applications in multiple times, and protects users' privacy while providing the users with accurate mobile application recommendations. The recommendation information is generated based on the fusion and filtering of the real behavior data that the user uses the mobile application. The recommended result can reflect the user's real preference to the greatest extent with a higher personalization, and the accuracy of the recommendation is greatly improved. The security and reliability of the system are improved during the process of filtering and converging data. In the invention, the privacy data of the user can be protected sufficiently and securely. The whole process adopts a scheme for protecting user privacy data, and the accuracy and personalization of the recommendation result are ensured, thereby ensuring the security, reliability and ease ofoperation of the system.

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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