图书情报工作 ›› 2020, Vol. 64 ›› Issue (5): 50-59.DOI: 10.13266/j.issn.0252-3116.2020.05.006

• 理论研究 • 上一篇    下一篇

基于小数据的社交类学术App用户动态画像模型构建研究

张莉曼1, 张向先1, 吴雅威1, 郭顺利2   

  1. 1. 吉林大学管理学院 长春 130022;
    2. 曲阜师范大学传媒学院 日照 276826
  • 收稿日期:2019-06-25 修回日期:2019-09-19 出版日期:2020-03-05 发布日期:2020-03-05
  • 作者简介:张莉曼(ORCID:0000-0002-0770-3708),博士研究生,E-mail:326671265@qq.com;张向先(ORCID:0000-0003-3186-2677),教授,博士,博士生导师;吴雅威(ORCID:0000-0001-9703-8731),博士研究生;郭顺利(ORCID:0000-0002-3155-9937),讲师,博士。
  • 基金资助:
    本文系国家社会科学基金项目"大数据驱动下学术新媒体知识聚合及创新服务研究"(项目编号18BTQ085)研究成果之一。

Research on the Construction of Dynamic Portrait Model of Social Academic App Users Based on Small Data

Zhang Liman1, Zhang Xiangxian1, Wu Yawei1, Guo Shunli2   

  1. 1. School of Management, Jilin University, Changchun 130022;
    2. Media College, School of Qufu Normal University, Rizhao 276826
  • Received:2019-06-25 Revised:2019-09-19 Online:2020-03-05 Published:2020-03-05

摘要: [目的/意义] 基于小数据构建社交类学术App用户动态画像模型,为社交类学术App平台有效预测用户行为演化趋势、提高精准服务水平提供思路和参考。[方法/过程] 首先,在深度剖析小数据概念及特点的基础上,结合社交类学术App特征,从用户表层行为和深层驱动因素两方面设计动态画像标签体系;其次,采集与用户强相关、高价值的小数据作为画像的数据支撑,并明确画像小数据的获取及处理方法;最后提出实现动态画像的研究方法并形成整体框架模型。[结果/结论] 基于小数据构建社交类学术App用户动态画像可有效细化画像粒度,改善以往画像滞后性弊端,对数据驱动情境下社交类学术App平台提升精准服务水平有重要的参考价值。

关键词: 小数据, 社交类学术App, 用户动态画像, 行为预测

Abstract: [Purpose/significance] Enrich and expand the theoretical research system of building dynamic portrait of social academic App users based on small data, so as to provide ideas and reference for the social academic App platform to effectively predict the evolution trend of user behavior and improve the precise service level.[Method/process] Firstly, based on the deep analysis of concept and characteristics of the small data, combined with the feature of social academic App, this paper from two aspects of user behavior and the surface of deep factors designed dynamic portrait label system. Then collected the small data with strong correlation and high value with the user as the data support of the portrait, and clarified the acquisition and processing method. Finally, it put forward the research method to realize the dynamic portrait and form the overall frame model.[Result/conclusion] The construction of dynamic portrait of social academic App users based on small data can effectively refine the granularity of portrait, and improve the lag of previous portrait, which has important reference value for the promotion of accurate service level of social academic App platform under data-driven situation.

Key words: small data, social academic App, dynamic portrait, behavior prediction

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