User customer group classification method and device

Abstract

The invention provides a user customer group classification method and device, and the method comprises the steps: obtaining user feature data in participation nodes of a federated learning distributed network, wherein the federated learning distributed network comprises participation nodes and model aggregation nodes; training a logistic regression model in the participation nodes according to the user characteristic data, and determining participation node gradient ciphertext information; uploading the participation node gradient ciphertext information to a model aggregation node of the federated learning distributed network for aggregation, and determining aggregation gradient ciphertext information; according to the aggregation gradient ciphertext information, carrying out transverse federated learning in model aggregation nodes, and determining joint gradient information; distributing the joint gradient information to each participation node, and inputting the joint gradient information to a federated learning logistic regression customer group classification model for training; and classifying the user customer groups according to the trained federated learning logistic regression customer group classification model. According to the invention, the accuracy of user customer group classification can be improved.

Description

technical field [0001] The invention relates to the technical field of computer information processing, in particular to a method and device for classifying user groups. Background technique [0002] This section is intended to provide a background or context for implementations of the invention that are recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section. [0003] With the development of artificial intelligence theory and technology, a large amount of user information can be used to provide users with customized and personalized services that meet user preferences. The accuracy of machine learning models requires a large amount of training data and rich user characteristics as support. [0004] However, in view of data security and privacy protection of user data, on the one hand, it is currently impossible to share data among banks for training models, making the data distributed in independent "data islands"; In ...

AI Technical Summary

Problems solved by technology

[0004] However, in view of data security and privacy protection of user data, on the one hand, it is currently impossible to share data among banks for training models, making the data distributed in independent "data islands"; In the data used to train the customer group classification model, negative samples generally only account for a small proportion, and the positive and negative samples are extremely unbalanced. Existing solutions often adopt the method of reducing positive samples for this situation, but this also reduces The training sample size is reduced, resulting in a decline in the accuracy of the model, which makes the classification effect of the trained model mediocre.

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