Method, device and system for self-adaptively adjusting models in computer systems

Abstract

The invention relates to a method, device and system for self-adaptively adjusting models in computer systems. The method comprises the following steps: obtaining behavior data of the current observation point in a first performing stage; obtaining a prediction result output by a target model at the moment of a first observation point, wherein the first observation point is a starting time point of the first performing stage, and the prediction result is a probability estimation value, predicted by the target model, of a preset event occurring in the first performing stage; carrying out adjustment function training by utilizing the obtained behavior data and prediction result so as to obtain an adjustment function of the target model; and adjusting the prediction result output by the target model at the moment of the current observation point by utilizing the adjustment function of the target model. According to the method, device and system, self-adaptive adjustment can be carried out on the models in systems, so that adjustment response can be rapidly and efficiently carried out on the change of user behavior characteristics and the properties of the models of the systems is ensured.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a model adaptive adjustment method, device and system in a computer system. Background technique [0002] At present, among computer systems in various fields, more and more computer systems assist decision-making by deploying corresponding models. Models provide decision-making basis for various types of computer systems with their excellent prediction accuracy and sorting functions. [0003] However, since the model in the computer system is trained based on historical information, as time goes by, the samples will change and migrate, resulting in a decline in the accuracy of the model. [0004] In order to solve this problem of model performance degradation, the commonly used method is to rebuild the model after a period of time, adjust the variable parameters, or develop a brand new model, and then update or replace the original model in the system . [0005] Wh...

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Problems solved by technology

[0003] However, since the model in the computer system is trained based on historical information, as time goes by, the samples will change and migrate, resulting in a decline in the accuracy of the model

[0004] In order to solve this problem of model performance degradation, the commonly used method is to rebuild the model after a period of time, adjust the variable parameters, or develop a brand new model, and then update or replace the original model in the system

[0005] Whether it is rebuilding a model or developing a brand new model, a lot of resources are required. Moreover, it takes a certain amount of time for the model to be re-deployed and launched online, resulting in a certain lag in the update of the model.

In some application fields, due to the rapid changes in user behavior characteristics, the model needs to be able to adjust the response quickly and efficiently, and the method of model reconstruction cannot meet this requirement.

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