Information push method, device, equipment, medium and computer product

By presetting the mapping relationship between the recommendation model and the algorithm template, the problem of low efficiency in recommendation model configuration is solved, and a faster information push development cycle and higher efficiency are achieved.

CN114329204BActive Publication Date: 2025-09-19AGRICULTURAL BANK OF CHINA
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Patent Information

Application Number
CN202111633474.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2021-12-28
Publication Date
2025-09-19
Estimated Expiration
2041-12-28

AI Technical Summary

Technical Problem

In existing technologies, recommendation models require customized development during configuration, resulting in low overall efficiency of information push.

Method used

By mapping the preset recommendation model and the preset algorithm template, target push information is generated, which reduces the development cycle and improves efficiency.

Benefits of technology

By presetting the mapping relationship between recommendation models and algorithm templates, the development cycle is shortened and the overall efficiency of information push is improved.

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Abstract

The present invention provides an information push method, apparatus, device, medium, and computer product, the method comprising: obtaining push-related data corresponding to a user terminal to be pushed; inputting the push-related data into a preset recommendation model to output target push information; the preset recommendation model is generated based on model construction-related data and a preset algorithm template; the preset algorithm template is determined based on a preset recommendation model category; a mapping relationship exists between the preset recommendation model category and the preset algorithm template; and the target push information is sent to the user terminal. The information push method of the present invention has a shorter development cycle and higher efficiency because the preset recommendation model can be generated based on model construction-related data and a preset algorithm template, thereby improving the overall efficiency of information push.
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