A user segmentation approach for ugc platform based on a new lead user identification index system and K-means clustering

D. Chang, J. Zhao, F. Zou, G. Xu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Nowadays, user-generated content (UGC) has become an important part of Internet user data. This study aims to develop an innovative user identification approach based on UGC platforms. To achieve the objective, this research proposed i) a web mining process to crawl UGC data; ii) a lead user identification index system for evaluating the innovation capability of users; and iii) a user classification process based on K-means clustering according to their UGC performance. Particularly, the complete user performance data of more than 100 users on Douban (one of the biggest UGC platforms in China) were collected, and the web mining, factor analysis, and clustering algorithm was integrated to process the data and classify user groups according to their UGC performance. The classification results were verified through incorporating expertise, and it showed that the classification can exactly recognize the users with proper lead userness. This research is expected to help small and medium enterprises without powerful big data ability to identify innovative users and valuable UGC data more efficiently and facilitate the further product improvement.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
PublisherIEEE Computer Society
Pages954-958
Number of pages5
ISBN (Electronic)9781538672204
DOIs
Publication statusPublished - 14 Dec 2020
Externally publishedYes
Event2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020 - Virtual, Singapore, Singapore
Duration: 14 Dec 202017 Dec 2020

Publication series

NameIEEE International Conference on Industrial Engineering and Engineering Management
Volume2020-December
ISSN (Print)2157-3611
ISSN (Electronic)2157-362X

Conference

Conference2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
Country/TerritorySingapore
CityVirtual, Singapore
Period14/12/2017/12/20

Keywords

  • Factor analysis
  • Innovative users
  • K-means clustering
  • Lead user identification
  • UGC

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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