User group partitioning method and device

Abstract

The embodiment of the invention provides a user group partitioning method and device. The method comprises the steps of capturing user identifiers; building a joint behavior relation between the user identifiers; obtaining a user node diagram; in the user node diagram, recognizing one or more core user groups according to the joint behavior relation; in the one or more core user groups, dividing one or more target user groups according to the joint behavior relation. On one hand, manual rule setting is avoided, different user groups have different distribution features, although individual behavior differences are large, the potential relation of the user is more stable, and the user group partitioning accuracy of the user groups is improved in a diagram-based user group partitioning mode; on the other hand, by roughly partitioning the core user groups, the data size is greatly decreased, the partitioning efficiency is improved, and the user group partitioning accuracy is improved.

Description

technical field [0001] The present application relates to the technical field of computer processing, in particular to a user group division method and a user group division device. Background technique [0002] With the rapid development of the Internet, the amount of information on the Internet has increased dramatically. The excessive information makes it impossible for people to efficiently obtain what they need, and the efficiency of information use decreases instead. [0003] Therefore, major websites usually divide their oriented users into different user groups to provide more refined services. [0004] In addition, in some security detection scenarios, users also need to be divided into different user groups. [0005] For example, on e-commerce websites, criminals maliciously increase store points through virtual transactions and other means, commonly known as "swiping diamonds". In order to maintain order, the website needs to identify the group of "swiping diamon...

AI Technical Summary

Problems solved by technology

[0011] In the community discovery algorithm, it is easy to introduce data that does not conform to the specific application scenario into the results, resulting in excessive data volume, low division efficiency, and low accuracy of user group division

[0012] Taking the identification of the "diamond brush" group as an example, the purchase relationship between users is first abstracted when modeling. Simply thinking that two users have purchased a certain product together means that they have a relationship, which will make the scale of the established graph too large. Large, resulting in low division efficiency, and there is a risk of misidentifying some users with a large number of purchases as "drilling" users

Images