Frontiers of Data and Domputing ›› 2022, Vol. 4 ›› Issue (3): 124-130.

CSTR: 32002.14.jfdc.CN10-1649/TP.2022.03.009

doi: 10.11871/jfdc.issn.2096-742X.2022.03.009

• Technology and Application • Previous Articles     Next Articles

Predictive Model of the Revisit Behavior of Cloud Service Site Users

WEI Ting(),ZHANG Honghai(),LIN Xiaoli(),ZHANG Leilei(),WANG Yan(),JIA Jinfeng()   

  1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
  • Received:2021-07-26 Online:2022-06-20 Published:2022-06-20
  • Contact: WEI Ting;;;;;


[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.

Key words: user behavior, prediction, machine learning, logistic regression, decision tree