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OCPX adaptive learning method and system based on fast moving consumer arrival in industry of fast moving consumer arrival

A self-adaptive learning and user technology, applied in machine learning, marketing, business, etc., can solve problems affecting the effect of delivery, achieve the effect of optimizing delivery effect and user experience

Pending Publication Date: 2021-04-13
恩亿科(北京)数据科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present application provides an OCPX adaptive learning method and system based on user touch in the fast-moving consumer industry, to at least solve the problem in related technologies that the direct connection between clicks and post-conversions cannot be directly confirmed to affect the delivery effect

Method used

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  • OCPX adaptive learning method and system based on fast moving consumer arrival in industry of fast moving consumer arrival
  • OCPX adaptive learning method and system based on fast moving consumer arrival in industry of fast moving consumer arrival
  • OCPX adaptive learning method and system based on fast moving consumer arrival in industry of fast moving consumer arrival

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Embodiment 2

[0244] Based on the first embodiment, this embodiment can monitor and collect different characteristic behaviors to perform similarity behavior matching.

[0245] But in essence, the collection of behavior monitoring logs is carried out on different devices, looking for relevant features (the specific feature content can be replaced) and then positively screening the target ID from the candidate set.

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Abstract

The invention relates to an OCPX adaptive learning method and system based on fast moving consumer arrival, and the method comprises a data extraction step: enabling an algorithm platform to extract advertisement putting data and conversion data based on a behavior monitoring data log; a feature extraction step: selecting a corresponding sample from the conversion data according to the advertisement putting data, and performing feature extraction according to the sample; a model training step of training a first-second classification model according to an extreme gradient boosting tree principle based on the features; a result uploading step: sequentially scoring the user packets according to the binary classification model, selecting a part of users in the user packets as prediction results according to scoring results, and uploading the prediction results to a front-end processor; and a result query step, in which the advertisement service party completes query of the prediction result and corresponding operation through the front-end processor. The delivery effect and user experience are optimized through a self-adaptive state formed by a process closed loop from extraction of self-owned data to training and pushing to a front-end processor for advertisement query to carry out data query.

Description

technical field [0001] This application relates to the field of advertising decision-making technology, in particular to the OCPX adaptive learning method and system based on user touch in the fast-moving consumer industry. Background technique [0002] Under the current Internet environment, when customers purchase online advertisements through advertising service providers, through guaranteed price and quantity, and priority purchases, they cannot be directly linked to the direct conversion indicators of customers. The actual conversion business indicators are intelligently optimized to purchase traffic, resulting in a waste of budget, and the corresponding advertising audience will also be intruded by useless advertisements, resulting in a contradiction between supply and demand. The reasons for this situation include: the supply side has a situation where a large amount of budget is wasted due to repeated delivery of invalid advertisements and the inability to effectivel...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06N20/00G06K9/62
CPCG06Q30/0271G06Q30/0255G06Q30/0201G06N20/00G06F18/241G06F18/214
Inventor 景艳山姚俊盛
Owner 恩亿科(北京)数据科技有限公司
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