User classification method and device

Abstract

The embodiment of the invention provides a user classification method and device. The method comprises the steps of obtaining an identifier set formed by application identifiers of applications installed by a target user, a word segmentation set after name word segmentation of the applications, and a label set formed by category labels of the applications; inputting the identifier set, the segmented word set and the label set into a pre-trained neural network model, obtaining a first embedded vector corresponding to each application identifier in the identifier set, obtaining a second embeddedvector corresponding to each segmented word in the segmented word set, and obtaining a third embedded vector corresponding to each category label in the label set; performing weighting processing oneach first embedded vector to obtain a fourth embedded vector; performing weighting processing on each second embedded vector to obtain a fifth embedded vector; performing weighting processing on eachthird embedded vector to obtain a sixth embedded vector; and determining a category corresponding to the target user according to the fourth embedded vector, the fifth embedded vector and the sixth embedded vector. The users can be accurately classified.

Description

technical field [0001] One or more embodiments of this specification relate to the field of computers, and in particular, to a method and device for classifying users. Background technique [0002] At present, Internet services often involve classifying users, determining whether to provide services to the users according to the types of users, or determining the service level to be provided to the users according to the types of users. For example, in the micro-loan business, in order to minimize the capital loss caused by overdue, before granting loans to users, determine the user's category, so as to predict whether the user has the risk of overdue repayment as accurately as possible. [0003] In the existing technology, users are often classified based on their historical behavior data. The above historical behavior data may include users’ shopping habits, financial management habits, etc., but when the user’s historical behavior data cannot be obtained, it is impossible...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, users are often classified based on their historical behavior data. The above historical behavior data may include users’ shopping habits, financial management habits, etc., but when the user’s historical behavior data cannot be obtained, it is impossible to accurately classify users

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