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Behavior prediction model training method and device

A prediction model and training method technology, applied in the computer field, can solve problems such as limited speed and accuracy of prediction, single way of predicting user behavior, etc., to achieve the effects of reducing performance impact, precise crowd redirection, and improving user experience

Active Publication Date: 2020-09-18
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Claims
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AI Technical Summary

Problems solved by technology

However, the current method of predicting user behavior is relatively simple, and the speed and accuracy of prediction are also very limited.

Method used

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  • Behavior prediction model training method and device
  • Behavior prediction model training method and device
  • Behavior prediction model training method and device

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Embodiment Construction

[0022] Multiple embodiments disclosed in this specification will be described below in conjunction with the accompanying drawings.

[0023] The embodiment of this specification discloses a training method of a behavior prediction model. Below, the inventive concept of the training method will be introduced first, as follows:

[0024] As mentioned above, it is hoped that the accuracy and timeliness of user behavior prediction can be improved. However, there is often a conflict between prediction accuracy and timeliness, because the more parameters learned by the machine, the higher the prediction accuracy will be, but more parameters will bring more calculations, which will lead to The decrease in speed, especially in the case of a large number of users, will cause more obvious delays.

[0025]In order to solve the conflict between accuracy and timeliness and realize the simultaneous improvement of both, the inventor proposes to use the framework of KD (Knowledge Distillation,...

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Abstract

The embodiment of the invention provides a behavior prediction model training method. The method comprises the steps: firstly determining a plurality of sample users for a target object, enabling anyfirst sample user to correspond to a first sample hard tag, and indicating whether a specific behavior is carried out on the target object or not; performing graph embedding processing on the user-object bipartite graph based on the trained graph neural network to determine an embedding vector set; determining a sample user feature vector corresponding to the first sample user and a target objectfeature vector corresponding to the target object, determining a specific behavior probability that the first sample user makes a specific behavior to the target object as a first sample soft label, and inputting the sample user feature vector into a first behavior prediction model to obtain a behavior prediction result; and training the first behavior prediction model by using a first loss item determined based on the prediction result and the first sample hard tag and a second loss item determined based on the prediction result and the first sample soft tag.

Description

technical field [0001] The embodiments of this specification relate to the field of computer technology, and in particular to a method and device for training a behavior prediction model. Background technique [0002] Currently, the service platform usually recommends or pushes business objects such as products or content to users, for example, recommending some online courses, clothing products, advertisement pictures, and the like. With the cumulative growth of the number of business objects and the continuous emergence of new business objects, in order to improve user experience, it is necessary to promptly and accurately recommend business objects that meet their needs and preferences to users. Accordingly, the service platform can use machine learning models to predict User behavior, specifically predicting whether a certain user will perform a specific behavior on a certain business object, so as to determine whether to recommend the certain business object to the cert...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06K9/62G06N3/04G06N3/08
CPCG06Q30/0202G06N3/08G06N3/045G06F18/23213G06F18/2415G06F18/24323G06F18/253
Inventor 庄晨熠张志强刘子奇周俊谭译泽魏建平刘致宁吴郑伟顾进杰漆远张冠男
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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