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

Abstract

The invention provides a method for training a recommendation probability prediction model and a recommendation probability prediction method and device, and relates to the technical field of artificial intelligence, and the method comprises the steps: inputting sample data obtained from a sample data set into the recommendation probability prediction model in the multi-round iterative training process of the recommendation probability prediction model, obtaining a prediction recommendation result corresponding to the sample data; obtaining a corresponding basic loss value according to a predicted recommendation result and an actual recommendation result corresponding to the sample data; obtaining a corresponding target loss value based on the basic loss value and the first adjustment value, wherein the first adjustment value is used for representing the total difference degree of output results, obtained based on the corresponding sample data, of every two task networks, and the first adjustment value is in negative correlation with the target loss value; and according to the target loss value, performing parameter adjustment on the recommendation probability prediction model to improve the prediction performance of the trained recommendation probability prediction model so as to improve the prediction accuracy of the to-be-predicted information.

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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