Method and apparatus for evaluating label data

Abstract

The invention discloses a method and a device for evaluating label data, and relates to the technical field of computers. A specific implementation of the method includes: displaying specific information to the user according to a plurality of tag data to be evaluated; evaluating the tag data to be evaluated as qualified when the display effect data meets the preset first judging condition; and / or, According to the label data to be evaluated, specific information is displayed to the user, and the effect data of the specific information display is determined as the initial effect data; The data displays specific information to the user, and the effect data displayed by the specific information is determined as the dislocation effect data; when the initial effect data is better than the dislocation effect data, the label data to be evaluated is evaluated as qualified. This embodiment can accurately evaluate the quality of label data without label data.

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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