数据与计算发展前沿 ›› 2020, Vol. 2 ›› Issue (6): 74-81.

doi: 10.11871/jfdc.issn.2096-742X.2020.06.008

• 技术与应用 • 上一篇    下一篇

中国科技云门户用户忠诚度的研究

危婷(),张宏海,蔺小丽,张蕾蕾,王妍,贾金峰,吴笛   

  1. 中国科学院计算机网络信息中心,北京 100190
  • 收稿日期:2020-08-05 出版日期:2020-12-20 发布日期:2020-12-29
  • 通讯作者: 危婷
  • 作者简介:危婷,中国科学院计算机网络信息中心,博士,高级工程师,主要研究方向为数据分析、云资源调度算法。本文中负责撰稿,中国科技云用户行为数据分析和建模。
    WEI Ting, Ph.D., is a senior engineer of Computer Network Information Center, Chinese Academy of Sciences. Her recent research interests focus on the unified scheduling of cloud resources and the research and development of cloud service platform. Her main research direction is data analysis and cloud resource scheduling algorithm. In this paper, she is responsible for the paper writing, data analysis and modeling user behaviors on CSTCloud.E-mail: weiting@cnic.cn|张宏海,中国科学院计算机网络信息中心,硕士,副研究员,科技云发展部云服务软件研发业务室主任,主要研究方向为云资源的统一调度和云服务平台的研发。本文中负责总体统稿、科技云用户行为分析系统设计与应用。
    ZHANG Honghai is an associate researcher of Computer Network Information Center, Chinese Academy of Sciences, and the Director of Cloud Service Software Research and Development Business Department of Science and Technology Cloud Development Department. His main research directions are the unified scheduling of cloud resources and the research and development of cloud service platform.In this paper, he is responsible for the final compilation, and the design and application of CSTCloud user behavior analysis system.E-mail: zhh@cnic.cn|蔺小丽,中国科学院计算机网络信息中心,硕士,工程师,目前主要从事系统部署、数据采集、数据库构建的工作。本文中负责用户行为分析系统数据采集部分。LIN Xiaoli is an engineer of Computer Network Information Center, Chinese Academy of Sciences. She mainly engages in system deployment, data acquisition and database construction.In this paper, she is responsible for data acquisition of user behavior analysis system.E-mail: linxiaoli@cnic.cn|张蕾蕾,中国科学院计算机网络信息中心,硕士,工程师,目前主要从事前端开发的工作。本文中负责用户行为分析系统的开发。
    ZHANG Leilei is an engineer of Computer Network Information Center, Chinese Academy of Sciences. She mainly engages in front-end development. In this paper, she is responsible for the development of user behavior analysis system.E-mail: zhangleilei@cnic.cn|王妍,中国科学院计算机网络信息中心,硕士,工程师,目前主要从事前端开发的工作。本文中负责用户行为分析系统的开发。
    WANG yan is an engineer of Computer Network Information Center, Chinese Academy of Sciences. She mainly engages in front-end development. In this paper, she is responsible for the development of user behavior analysis system.E-mail: wangyan@cnic.cn|贾金峰,中国科学院计算机网络信息中心,硕士,工程师,目前主要从事系统开发的工作。本文中负责用户行为分析系统的开发。
    Jia Jinfeng is an engineer of Computer Network Information Center, Chinese Academy of Sciences. He mainly engages in system development.In this paper, he is responsible for the development of user behavior analysis system.E-mail: jiajinfeng@cnic.cn|吴笛,中国科学院计算机网络信息中心,硕士,目前主要从事系统开发的工作。本文中负责用户行为分析系统的开发。
    WU Di is an associate engineer of Com-puter Network Information Center, Chinese Academy of Sciences. She mainly engages in system development.In this paper, she is responsible for the development of user behavior analysis system.E-mail: wudi@cnic.cn
  • 基金资助:
    中国科学院“十三五”信息化专项中国科技云工程项目(XXH13503)

User Loyalty Study of CSTCloud

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

  1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2020-08-05 Online:2020-12-20 Published:2020-12-29
  • Contact: WEI Ting

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摘要：

【背景】中国科技云(CSTCloud)是一个面向科学工作者提供资源和服务的云平台,汇聚了包括云计算、云存储、高性能计算、科学软件等多种资源,自2018年4月发布以来受到了广泛关注。中国科技云门户作为资源的汇聚和入口,是用户获取和使用资源的重要途径。【方法】为了解用户在此平台的行为特征和粘性,基于中国科技云门户用户的真实访问数据,探索性提出改进的RFM模型来量化评定用户忠诚度的方法。【结果】此模型结合了现有用户行为数据的特点,并突出了近度指标Recency与用户价值的反向关系;此外,基于实际的忠诚度分析的结果,对用户群的粘性进行分类,以评估用户群的潜在价值。【结论】本文的研究基于现有的用户真实访问数据,有利于对用户群进行分类和个性化的运营决策,以便更好地为科技云用户提供服务。

关键词: 中国科技云, 用户行为, 忠诚度, RFM模型

Abstract:

[Background] China Science and Technology Cloud (CSTCloud) has been a widely concerned cloud platform which aggregates resource of cloud computing, cloud storage and high performance computing for scientists since its establishment in April, 2018. As the entrance of aggregated resources, CSTCloud is of great importance for users to obtain and utilize resources. [Methods] In order to understand user activities and stickiness, this paper develops an improved RFM model to evaluate loyalty of users based on real data of CSTCloud visitors. [Results] The model reflects the user data characteristics and the negative correlation between Recency index and the user value. In addition, users are classified by real loyalty analysis results, assessing the potential value of users for CSTCloud. [Conclusions] Based on real data, this study can help the CSTCloud operator to learn about user activity characteristics and to provide personalized services for classified users.

Key words: CSTCloud, user activity, user loyalty, RFM model