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Prediction method for individual behavior insight of WaveNet technology based on simulated annealing thought

A technology of simulated annealing and prediction methods, applied in the field of artificial intelligence in the field of Internet marketing, can solve problems such as unfavorable model training and low quality, and achieve the effect of ensuring effectiveness and improving accuracy

Pending Publication Date: 2022-04-12
上海数鸣人工智能科技有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] However, if the quality of new samples generated by data enhancement is not high, for example, too much noise is introduced, it is not conducive to the training of the model

Method used

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  • Prediction method for individual behavior insight of WaveNet technology based on simulated annealing thought
  • Prediction method for individual behavior insight of WaveNet technology based on simulated annealing thought
  • Prediction method for individual behavior insight of WaveNet technology based on simulated annealing thought

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Embodiment Construction

[0047] Attached below Figure 1-5 , the specific embodiment of the present invention will be further described in detail.

[0048] In the following specific embodiments, when describing the embodiments of the present invention in detail, in order to clearly show the structure of the present invention for the convenience of description, the structures in the drawings are not drawn according to the general scale, and local enlargement, deformation and Simplification, therefore, should be avoided as a limiting interpretation of the invention.

[0049] see figure 1 , figure 1 Shown is a schematic flow chart of the prediction method of the WaveNet technology based on the idea of ​​simulated annealing in the present invention for individual behavior insights. Such as figure 1 As shown, the prediction method includes a data preprocessing step S1, a training set generation step S2, a prediction model establishment step S3, and a marketing activity prediction step S4.

[0050] In th...

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Abstract

The invention discloses a marketing activity prediction method for an individual behavior insight model based on a WaveNet technology of a simulated annealing thought. The method comprises a data preprocessing step, a training set generation step, a prediction model establishment step and a marketing activity prediction step. According to the method, a sample is expanded through a WaveNet model, the sample comprises two parts of input feature sequences, the first input feature sequence is composed of original feature information of a user, and the second input feature sequence is a feature sequence formed by predicting and optimizing the original feature information through a WaveNet network framework; and meanwhile, the simulated annealing algorithm ensures the effectiveness of the new sequence, and the accuracy of predicting the click behavior of the user is remarkably improved.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence in Internet marketing, and more specifically, to a prediction method for individual behavior insights based on the WaveNet technology based on the idea of ​​simulated annealing. Background technique [0002] Click-Through Rate (CTR) estimation is a key link in Internet computing advertising; the accuracy of user estimation directly affects the company's marketing advertising revenue. The problem of click-through rate estimation is a key link in Internet computing advertising, and the accuracy of user estimation directly affects the company's marketing advertising revenue. [0003] In the process of advertising, there are generally three processes: ad exposure, user click, and user registration (or conversion). One of the main purposes of CTR estimation is to improve the efficiency from ad exposure to user click, that is, to increase the click-through rate. Since a user cli...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06K9/62G06N3/08
Inventor 项亮方同星
Owner 上海数鸣人工智能科技有限公司