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Method and device for training event prediction model

A technology for predicting models and events, applied in computing models, neural learning methods, biological neural network models, etc., can solve problems such as limited evaluation accuracy and single evaluation methods

Active Publication Date: 2020-10-23
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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Problems solved by technology

However, the current evaluation method is relatively simple, resulting in very limited evaluation accuracy

Method used

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  • Method and device for training event prediction model
  • Method and device for training event prediction model
  • Method and device for training event prediction model

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Embodiment approach

[0064] According to an implementation manner, the above-mentioned first combination further includes an N-order inter-vector combination operation involving multiplication of N coded vectors, where N>=2.

[0065] It can be understood that the combination of feature vectors in conventional neural networks generally adopts a linear combination method. However, when an event contains multiple attribute information, sometimes the attribute information is not completely independent, but there is a certain dependency or correlation, and a simple linear combination is not enough to discover and process such a correlation. Therefore, in one embodiment, referring to the framework of the FM factorization machine, a high-order inter-vector combination operation is introduced in the first embedding layer.

[0066] The N-order inter-vector combination operation involves the multiplication operation of N coded vectors, so that the association relationship between the N coded vectors can be ...

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Abstract

The embodiment of the invention provides a method for training an event prediction model, the method can be applied to a transfer learning scene, and data isolation and privacy security protection ofa source domain participant and a target domain participant are realized by setting a neutral server, wherein the source domain participant deploys a source domain feature extractor, the target domainparticipant deploys a target domain feature device, and a model sharing part in an event prediction model is deployed in a neutral server and specifically comprises a sharing feature extractor, a graph neural network and a classification network. For any participant, feature extraction is performed on a sample in a local domain by utilizing a feature extractor of the local domain to obtain localdomain feature representations, and the local domain feature representation is processed by using the current parameters of the model sharing part obtained from the server to obtain a corresponding event classification result, model updating based on the event classification result and the local domain sample is performed, and an updating result of the model sharing part is uploaded to the serverto enable the server to perform centralized updating.

Description

technical field [0001] One or more embodiments of this specification relate to the field of machine learning, and in particular, to a method and device for training an event prediction model using machine learning. Background technique [0002] In many scenarios, user operation behavior or operation events need to be analyzed and processed. For example, in order to identify high-risk operations that may threaten network security or user information security, such as account theft, traffic attacks, fraudulent transactions, etc., the risk level of user operations can be assessed for risk prevention and control. [0003] In order to assess the risk degree of an operation behavior, it can be analyzed based on the characteristics of the operation behavior itself. Further, the user's behavior sequence can also be considered more comprehensively. Behavior sequence is the occurrence process of a series of clicks, visits, purchases and other events generated by users in daily opera...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06N20/00G06K9/62
CPCG06N3/08G06N20/00G06F18/241G06F18/214
Inventor 宋博文顾曦陈帅
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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