User group optimization method and device, storage medium and computer equipment

Abstract

The invention discloses a user group optimization method and device, a storage medium and computer equipment. The method comprises the steps that a target user group set and a user group total set obtained by clustering a decision tree model are acquired, the target user group set comprises a first user group corresponding to each leaf node of the decision tree model, and the user group total setcomprises a second user group corresponding to each node of the decision tree model; if the number of the target quality users in the target user group set is smaller than a preset coverage thresholdvalue; if so, a new target user group set is generated according to the number of grouping rules corresponding to the first user group and a second user group corresponding to the father node of the first user group node in the user group total set, wherein the number of target quality users in the new target user group set is greater than or equal to a preset coverage threshold; and a final usergroup is determined according to the new target user group set. The user group optimization method is simple and effective.

Description

technical field [0001] The present application relates to the technical field of data processing, in particular to a user group optimization method and device, storage medium, and computer equipment. Background technique [0002] In the existing decision tree-based user grouping methods, in order to ensure the proportion of the target group for each user group, many grouping rules are usually set for the decision tree classification model, so that each user group corresponds to multiple grouping rules, However, judging from the results of grouping, this overly detailed grouping rule will lead to a small number of users retained in each user group, and overall, the target group of people covered by all user groups cannot reach the expected number. [0003] In the existing user group optimization methods, the above problems are usually solved by re-establishing the grouping rules and retraining the decision tree. This method will consume a lot of time when the grouping rules a...

AI Technical Summary

Problems solved by technology

[0002] In the existing decision tree-based user grouping methods, in order to ensure the proportion of the target group for each user group, many grouping rules are usually set for the decision tree classification model, so that each user group corresponds to multiple grouping rules, However, judging from the results of grouping, this overly detailed grouping rule will lead to a small number of retained users in each user group. Overall, the target group covered by all user groups cannot reach the expected number.

[0003] In the existing user group optimization methods, the above problems are usually solved by re-establishing the grouping rules and retraining the decision tree. This method will consume a lot of time when the grouping rules are complex and the amount of user data is large.

At present, there is no method that can quickly and effectively optimize the user base

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