Shop category identification model generation method and device and shop category identification method and device

Abstract

The invention discloses a store category recognition model generation method and device and a store category recognition method and device, and the store category recognition model generation method comprises: obtaining sample commodity information of a sample store and multistage service categories corresponding to the sample commodity information; determining a plurality of identification dimensions for shop category identification, at least one branch node corresponding to each identification dimension and category identification constraint information corresponding to each identification dimension; generating target identification information of the sample shop under each identification dimension based on the sample commodity information and the multi-level business category corresponding to the sample commodity information; based on the influence factor of each identification dimension and the corresponding at least one branch node, constructing a preset tree structure corresponding to shop category identification; and performing shop category identification training on the preset tree structure based on the target identification information and the category identification constraint information to obtain a shop category identification model. According to the invention, the accuracy of shop category identification can be improved, and the risk of manual operation errors is reduced.

Description

technical field [0001] The present application relates to the field of data processing, in particular to a method and device for generating a shop category recognition model and shop category recognition. Background technique [0002] The traditional store category identification method manually marks the store category, relies on the business personnel to manually click on the store link, and subjectively defines the store category according to the number of commodities and commodity categories under the store. [0003] For the traditional method of manually labeling store categories, due to the large amount of product data and the complex and varied product level classification system under the store, manually clicking on the store link not only requires a lot of manpower, but also leads to inefficient category annotation. Artificial subjectivity is prone to produce wrong labeling results. Based on the low efficiency and high risk shown by the method of manually labeling ...

AI Technical Summary

Problems solved by technology

[0003] For the traditional method of manually labeling store categories, due to the large amount of product data on sale and the complex and varied product level classification system under the store, manually clicking on the store link not only requires a lot of manpower, but also leads to inefficient category annotation. Artificial subjectivity, easy to produce wrong labeling results

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