Research and application of prediction model for returning home development population based on machine learning technology

DU Zhao; XIE Guocheng; CHEN Jingxuan; ZHANG Weibin

doi:10.11959/j.issn.1000-0801.2024140

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Research and application of prediction model for returning home development population based on machine learning technology

Research and Development | 更新时间：2024-08-05

- Research and application of prediction model for returning home development population based on machine learning technology
- Telecommunications Science Vol. 40, Issue 5, Pages: 131-140(2024)
- 作者机构：
  
  1.中国电信股份有限公司广东分公司，广东广州 510062
  2.广东亿迅科技有限公司，广东广州 510627
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2024140
  CLC： TP391
- Received：17 January 2024，
  
  Revised：07 March 2024，
  
  Published：20 May 2024
- 稿件说明：
移动端阅览
杜昭,谢国城,陈静旋等.基于机器学习技术的返乡发展人群预测模型研究与应用[J].电信科学,2024,40(05):131-140.

DU Zhao,XIE Guocheng,CHEN Jingxuan,et al.Research and application of prediction model for returning home development population based on machine learning technology[J].Telecommunications Science,2024,40(05):131-140.
杜昭,谢国城,陈静旋等.基于机器学习技术的返乡发展人群预测模型研究与应用[J].电信科学,2024,40(05):131-140. DOI： 10.11959/j.issn.1000-0801.2024140.

DU Zhao,XIE Guocheng,CHEN Jingxuan,et al.Research and application of prediction model for returning home development population based on machine learning technology[J].Telecommunications Science,2024,40(05):131-140. DOI： 10.11959/j.issn.1000-0801.2024140.

摘要

随着经济的发展和一线城市生活压力的增大，越来越多的人迁移城市以及返回家乡发展，为了高效服务用户和提升用户产品使用体验，提出基于LightGBM、CatBoost等算法来预测返乡发展人群，并进行了异构模型融合。通过模型对比，所提融合模型有更好的效果，可以为服务和产品提供依据，减少流失优化感知，提高市场保有率。

Abstract

With the development of the Chinese economy and the increasing pressure of living in first-tier cities

more and more young people choose to return to their hometowns for development. To efficiently serve users and improve their product usage experience

the use of algorithms such as LightGBM and CatBoost was proposed to predict the returning population

thereby providing a basis for services and products

and improving user market retention rates.

关键词

Keywords

references

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