Method for training recommendation probability prediction model, recommendation probability prediction method and device

Abstract

This application provides a method for training a recommendation probability prediction model, a recommendation probability prediction method and a device, which relate to the field of artificial intelligence technology. The sample data is input into the recommendation probability prediction model to obtain the predicted recommendation results corresponding to the sample data; the corresponding basic loss value is obtained according to the predicted recommendation results corresponding to the sample data and the actual recommendation results; based on the basic loss value and the first adjustment value, the corresponding The target loss value of ; wherein, the first adjustment value is used to represent the total difference degree of the output results of each two task networks obtained based on the corresponding sample data, and the first adjustment value is negatively correlated with the target loss value; according to the target The loss value is to adjust the parameters of the recommendation probability prediction model to improve the prediction performance of the trained recommendation probability prediction model, thereby improving the prediction accuracy of the prediction information to be predicted.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to a method for training a recommendation probability prediction model, a recommendation probability prediction method and a device. Background technique [0002] With the continuous development of Internet technology, various network information emerges in an endless stream, such as articles, videos, pictures, products, advertisements, etc., making information recommendation systems widely used. In practical applications, when recommending articles, videos, pictures and other information to the target object through the information recommendation system, the purpose is to achieve the following recommendation effect: the target object clicks on the recommended information, then reads the entire content of the recommended information, and likes it , forwarding, commenting and other interactions. [0003] In related technologies, in order to achieve the ...

AI Technical Summary

Problems solved by technology

However, the current multi-task learning model usually has the problem of the convergence of the output results of multiple neural networks, that is, the output results of multiple neural networks are very small, so that the prediction performance of the multi-task learning model after training is not high, which will treat The forecast accuracy of the forecast information is affected

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