Method and device for evaluating label data

Abstract

The invention discloses a method and device for evaluating label data, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps of: displayingspecific information to a user according to a plurality of pieces of to-be-evaluated label data; when the display effect data conforms to a preset first judgment condition, evaluating the to-be-evaluated label data to be qualified; and / or performing specific information display to the user according to the to-be-evaluated label data, and determining the effect data of the specific information display as initial effect data; performing dislocation processing on the to-be-evaluated label data and the user identifier in the to-be-evaluated label data, performing specific information display on the user according to the data obtained by the dislocation processing, and determining the effect data of the specific information display as dislocation effect data; and when the initial effect data is superior to the dislocation effect data, evaluating the to-be-evaluated tag data to be qualified. According to the embodiment, the quality of the label data can be accurately evaluated under the condition that the label data are not labeled.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and device for evaluating label data. Background technique [0002] In the field of computer technology, in order to achieve accurate matching of push information, user portraits are often required. User portrait refers to the abstraction of a tagged user model based on the user's social attributes, living habits, consumption behavior and other information, that is to say, setting a tag data containing multiple tag values ​​under multiple tags for the user. For example, according to a user's social attributes and consumption behavior, the following label data can be generated for the user: "gender: male; age: 32 years old; region: Shanxi; user ID: XXXXX; interest: sports, science"; among them, gender, Age, region, user ID, and interest are different tags, and male, 32 years old, Shanxi, XXXXX, sports, and science are tag values ​​under the corresponding tags. [0003...

AI Technical Summary

Problems solved by technology

[0005] 1. The evaluation of label data depends entirely on the label data, and the amount of label data in specific applications is small, making it difficult to effectively evaluate massive label data

[0006] 2. The label data is generally fixed, but the user's label data often changes with his behavior, so the evaluation method based on label data is difficult to achieve long-term monitoring of label data quality

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