云服务网站用户复访行为预测模型研究  

Predictive Model of the Revisit Behavior of Cloud Service Site Users

作　　者：危婷 张宏海[1] 蔺小丽 张蕾蕾 王妍[1] 贾金峰 WEI Ting;ZHANG Honghai;LIN Xiaoli;ZHANG Leilei;WANG Yan;JIA Jinfeng(Computer Network Information Center,Chinese Academy of Sciences,Beijing 100083,China)

机构地区：[1]中国科学院计算机网络信息中心,北京100083

出　　处：《数据与计算发展前沿》2022年第3期124-130,共7页Frontiers of Data & Computing

摘　　要：【目的】为了解用户的兴趣与需求,提升在线推荐和网站运营效果,利用用户浏览和操作等行为数据来预测用户行为具有重要的价值。【方法】通过云服务网站用户行为数据采集、特征选择、挖掘分析,基于逻辑回归(LR)算法和XGBoost决策树算法去训练用户行为模型,并对用户的复访行为进行预测分析。基于云服务网站真实用户行为数据对两个模型进行多维度的数值评估。【结果】发现LR模型的拟合度和准确率都更胜一筹,这与以往较多认为XGBoost模型更优的结果不同,这是由行为数据结构的特点造成的。【结论】本文的研究有利于对云服务网站用户复访行为进行预测,以对潜在价值用户制定个性化的运营决策,提升用户体验。[Objective]In order to know users'interests and needs,and improve the effectiveness of online recommendation and website operation,it is of great value to predict user behavior based on user browsing and operation behavior data.[Methods]Through data collection,feature selection,data mining,and analysis of the user behavior of the China Science and Technology Cloud(CSTCloud)website,the user revisit behavior can be predictable.The Logical Regression(LR)model and XGBoost model are trained respectively to predict the user revisit behavior,and multi-dimensional numerical evaluation is performed through real user behavior data.[Results]The results show that the LR model has better fitness and accuracy,which is different from the previous opinion that the XGBoost model is better.Identifying the characteristics of the behavioral data structure is the main reason.[Conclusions]The research in this paper is conducive to predict revisit behavior of CSTcloud website users,which enables personalized operation decisions for potential valuable users and improves user experience.

关 键 词：用户行为 预测 机器学习 逻辑回归 决策树 

分 类 号：TP393.092[自动化与计算机技术—计算机应用技术]