Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

User behavior prediction method and system and computer equipment

A prediction method and user technology, applied in computing, computing models, neural learning methods, etc., can solve problems such as changing information between features without considering time factors, low risk prediction accuracy, and inability to predict bad user behavior, etc. The effect of predicting bad user behavior and avoiding resource loss

Pending Publication Date: 2021-10-29
北京淇瑀信息科技有限公司
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The risk prediction accuracy of this method is low
[0004] In addition, none of the existing prediction methods consider the change information between features caused by time factors, so it is impossible to predict the occurrence of bad user behaviors caused by time factors, and it is also impossible to effectively avoid the resource loss caused by the occurrence of bad user behaviors.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • User behavior prediction method and system and computer equipment
  • User behavior prediction method and system and computer equipment
  • User behavior prediction method and system and computer equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0043] Below, will refer to Figure 1 to Figure 3 An embodiment of the user behavior prediction method of the present invention is described.

[0044] figure 1 It is a flowchart of an example of the user behavior prediction method of the present invention.

[0045] Such as figure 1 As shown, the user behavior prediction method includes the following steps.

[0046] Step S101, acquiring time-series feature data of the user to be predicted, the time-series feature data being user feature data corresponding to multiple time points within the resource return time.

[0047] In this step, the time series feature data of the user to be predicted at multiple time points within a specific time period are obtained, and the user behavior is predicted after data conversion is performed using the time series feature data.

[0048] Specifically, the time series feature data is user feature data corresponding to multiple time points within the resource return time, and the user feature d...

Embodiment 2

[0108] The system embodiment of the present invention is described below, and the system can be used to implement the method embodiment of the present invention. The details described in the system embodiments of the present invention should be regarded as supplements to the above method embodiments; details not disclosed in the system embodiments of the present invention can be implemented by referring to the above method embodiments.

[0109] refer to Figure 4 , Figure 5 and Figure 6 , the present invention also provides a user behavior prediction system 400 based on time series features, the user behavior prediction system 400 includes: a data acquisition module 401, used to acquire time series feature data of users to be predicted, the time series features The data is user feature data corresponding to multiple time points within the resource return time; the generation module 402 generates time-series feature evaluation data of the user to be predicted according to t...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a user behavior prediction method and system based on time sequence characteristics, and computer equipment. The method comprises the following steps: acquiring time sequence feature data of a user to be predicted, wherein the time sequence feature data is user feature data corresponding to a plurality of time points in resource return time; according to the time sequence feature data, generating time sequence feature evaluation data of the to-be-predicted user, the time sequence feature evaluation data being used for representing a change condition of the user feature data according to a time sequence; and based on a pre-trained machine learning model, according to the time sequence feature evaluation data, obtaining a prediction value of the user behavior occurrence probability of the to-be-predicted user. The user feature data can be more effectively utilized; and the feature evaluation data containing the feature change information caused by the time factor is used as the model input feature data, so that more accurate input feature data can be provided for model calculation, and the user behavior occurrence probability can be more accurately calculated.

Description

technical field [0001] The present invention relates to the field of computer information processing, in particular to a user behavior prediction method, system and computer equipment. Background technique [0002] Risk control (referred to as risk control) means that risk managers take various measures and methods to eliminate or reduce the various possibilities of risk events, or risk controllers reduce the losses caused by risk events. Risk control is generally used in the financial industry, such as risk control for company transactions, business transactions or personal transactions. [0003] In related technologies, a risk control method based on user login behavior analysis is disclosed. The method establishes and integrates user button risk identification, user login location risk identification, password retry risk identification, and device source risk identification through user login behavior. A model to obtain risk warning value. The risk prediction accuracy o...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q30/02G06N3/04G06N3/08G06N20/00
CPCG06Q10/0635G06Q30/0202G06N3/08G06N20/00G06N3/044G06N3/045
Inventor 姜润洲丁楠苏绥绥
Owner 北京淇瑀信息科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com