| 基于回归学习算法的高铁站媒体资源价值评估模型研究与应用 |
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| 引用本文： | 许娜,单杏花,付睿,吴刚,牛慧琳.基于回归学习算法的高铁站媒体资源价值评估模型研究与应用[J].铁路计算机应用,2022,31(12):20-25. |
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| 作者姓名： | 许娜 单杏花 付睿 吴刚 牛慧琳 |
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| 作者单位： | 1.中国铁道科学研究院集团有限公司 电子计算技术研究所，北京 100081 |
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| 基金项目： | 中国铁道科学研究院集团有限公司科研项目重点课题（2019YJ119） |
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| 摘 要： | 随着高速铁路（简称：高铁）车站媒体广告市场的兴盛，亟需一种科学、系统、全面的高铁站媒体资源价值评估体系指导媒体资源经营。文章研究价值评估指标体系的多维度数据与高铁站媒体资源价值的关系，借助特征工程，抽取出与目标强相关的核心数据特征。运用多种回归学习算法，筛选出评价指标最优的极限梯度提升（XGBoost）算法，构建高铁站媒体资源价值评估模型，通过模型优化，提升了拟合优度值，达到目标值0.8。应用证明，该模型偏离度不超过15%，可为高铁站媒体资源日常经营定价决策提供参考。
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| 关 键 词： | 价值评估 指标体系 极限梯度提升(XGBoost)算法 媒体资源 高铁车站 |
| 收稿时间： | 2022-01-21 |
Media resource value evaluation model of high-speed railway station based on regression learning algorithm |
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| Affiliation: | 1.Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China2.Operation and Development Department, CHINA RAILWAY, Beijing 100844, China |
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| Abstract: | With the prosperity of high-speed railway station media advertising market, a scientific, systematic and comprehensive evaluation system of high-speed railway station media resource value is urgently needed to guide the management of media resources. This paper studied the relationship between the multi-dimensional data of the value evaluation index system and the value of high-speed railway station media resources, with the help of feature engineering, extracted core data features that were strongly related to the target. Using a variety of regression learning algorithms, the paper selected the eXtreme Gradient Boosting (XGBoost) algorithm with the best evaluation index, and constructed the value evaluation model of high-speed railway station media resources. Through model optimization, the goodness of fit value was improved to reach the target value of 0.8. The application proves that the deviation degree of the model does not exceed 15%, which can provide reference for the daily operation and pricing decision of high-speed railway station media resources. |
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