A training method of a prediction model, a content promotion method, and related devices

By matching the model structure and evaluating hyperparameter combinations based on historical data from content promoters, the problem of low prediction model accuracy in existing technologies is solved, achieving higher prediction accuracy and automated hyperparameter selection.

CN122155786APending Publication Date: 2026-06-05TENCENT TECHNOLOGY (SHENZHEN) CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
TENCENT TECHNOLOGY (SHENZHEN) CO LTD
Filing Date
2024-12-03
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

In existing technologies, the prediction accuracy of the target prediction model trained is low, mainly due to the influence of factors such as sample comprehensiveness, model structure and learning rate, and the low adaptability of the prediction model caused by the differences in content characteristics of different content providers.

Method used

By acquiring historical promotion data from content promoters, a model structure matching their promotion positioning information is determined. Through multiple rounds of iterative training and evaluation of various hyperparameter value combinations, the hyperparameter combination that meets the training objectives is selected to form a target prediction model.

Benefits of technology

It improves the prediction accuracy of the prediction model, avoids the instability of prediction accuracy caused by factors such as sample comprehensiveness, model structure or learning rate, and enhances the automation and quantitative selection of hyperparameter settings.

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Abstract

The application provides a prediction model training method, a content promotion method and related equipment, and is used for solving the problem of low prediction accuracy of the prediction model. The method comprises the following steps: determining a model structure matched with structure description information and promotion positioning information of a content promoter extracted from historical promotion data as a prediction model of the content promoter based on the structure description information of a plurality of preset model structures and the promotion positioning information; training and evaluating the prediction accuracy of candidate prediction models set by a plurality of hyperparameters involved in the prediction model according to a plurality of preset hyperparameter value combinations, respectively, to obtain evaluation results corresponding to the plurality of hyperparameter value combinations, respectively; and obtaining a trained target prediction model based on a target value combination satisfying a training target based on the evaluation results. The prediction accuracy of the prediction model is improved through targeted prediction model training.
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