User evaluation method and device targeting application

Abstract

The invention discloses a user evaluation method and device targeting an application. The user evaluation method comprises steps of counting user attribute labels of a sample user and application programs installed by the sample user, calculating attribute inclination data of the application program, obtaining a usage condition of each application program installed by the target user, obtaining an attribute evaluation value of each application program installed by the target user according to the application program usage condition and attribute inclination data of the application program, obtaining a total evaluation value of the application program installed by the target user according to the attribute evaluation value of each application program installed by the target user, determining whether the total evaluation value is greater than a preset evaluation threshold value, and performing evaluation on the target user according to the determination result. The user evaluation method and device targeting the application can realize user evaluation according to the application, can screen out quality users having balance data, and greatly reduce user data imbalance.

Description

technical field [0001] The present invention relates to the technical field of mobile Internet, in particular to a user evaluation method and device for applications. Background technique [0002] With the advent of web2.0 and the rapid development of the mobile Internet, the basic attributes of users play an increasingly important role in network applications. For example, the personalized search service provided by Google is based on the user’s The history record returns a personalized search list for the user and provides the user with a personalized search service. This is because user attributes largely determine the user's intentions and habits, and knowing user attributes is of great significance for satisfying users' potential needs. The basic user attributes here usually refer to basic attributes such as the user's age, gender, income status, geographical location, education level, and religious belief. For the prediction of user attributes in mobile terminals, it...

AI Technical Summary

Problems solved by technology

[0003] In the process of implementing the present invention, the inventor found that the prior art has at least the following problems: the attribute categories of different users corresponding to different applications have huge differences, resulting in large differences between these user data. If there is no difference The selection of all users may lead to the imbalance of these user data, which will lead to inaccurate prediction and analysis results

Therefore, there is a lack of means in the prior art to evaluate the nature of the user data corresponding to the user, so that the acquired user data will appear relatively unbalanced

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