False quantity recognition model generation method, false quantity recognition method and computing device

Abstract

The invention discloses a false quantity recognition model generation method, a false quantity recognition method and a computing device. The false quantity recognition model generation method comprises the steps that role data corresponding to all role identifiers is acquired; data statistical analysis is performed on all the role data to generate first role statistical data corresponding to allIP addresses which are logged in and second role statistical data corresponding to all devices which are logged in; a false quantity IP address ratio and a false quantity device ratio corresponding toall the role data are calculated; all the role data is associated with the corresponding false quantity IP address ratio and false quantity device ratio to generate corresponding role extension data,clustering processing is performed on all the role extension data to acquire an optimal quantity of role clusters, and role cluster tags of all the role clusters are generated; role tags of all the role extension data are generated; and according to all the role data and the role tags associated with the role extension data corresponding to the role data, a neural network model constructed in advance is trained to generate a false quantity recognition model.

Description

technical field [0001] The invention relates to the technical field of the Internet, in particular to a method for generating a false quantity recognition model, a false quantity recognition method and a computing device. Background technique [0002] For game companies, when a developed game is launched, it usually promotes the game through an advertising platform, and users as the audience can download the game based on the link on the game advertising page, and then complete registration, login and payment, etc. A series of operations is more likely to become a long-term and stable game player. [0003] However, due to the difficulty and high cost of game promotion, many advertising platforms falsify user data to complete the pre-agreed number of active users with game companies, resulting in a large amount of untrue user data. Data is generally called fake data, which makes it difficult for game companies to analyze user data to optimize the game itself and formulate re...

AI Technical Summary

Problems solved by technology

[0003] However, due to the difficulty and high cost of game promotion, many advertising platforms falsify user data to complete the pre-agreed number of active users with game companies, resulting in a large amount of untrue user data. Data is generally called fake data, which makes it difficult for game companies to analyze user data to optimize the game itself and formulate related publicity, delivery and promotion strategies

In order to identify fake data, the behavior of individual users is usually identified one by one, but this method of identifying fake data for isolated individual behaviors has low identification efficiency and accuracy, and high labor costs

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