Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (4): 37-42.doi: 10.11896/jsjkx.210800255

• Special Issue of Social Computing Based Interdisciplinary Integration • Previous Articles     Next Articles

Identification and Segmentation of User Value in Crowdsourcing Platforms:An Improved RFMModel

CHEN Dan-hong, PENG Zhang-lin, WAN De-quan, YANG Shan-lin   

  1. School of Management, Hefei University of Technology, Hefei 230009, China; Key Laboratory of Process Optimization and Intelligent Decision, Ministry of Education, Hefei 230009, China
  • Received:2021-08-30 Revised:2021-12-09 Published:2022-04-01
  • About author:CHEN Dan-hong,born in 1996,postgraduate.Her main research interests include crowdsourcing users and cluster analysis.PENG Zhang-lin,born in 1984,Ph.D,associate professor.His main research interests include information resource management,swarm intelligence and so on.
  • Supported by:
    This work was supported by the Humanities and Social Science Research Foundation of Ministry of Education(16YJC630093) and National Natural Science Foundation of China(72071060,71601066,71901086).

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Abstract: On the crowdsourcing platform, different types of users have diversity and differences in participation intention, work motivation, business ability and other aspects, and the value they generated on the platform is also different.The segmentation of users based on user value measurement is the key to better insight into user value and needs for personalized and refined management of users.At the same time, the choice of crowdsourcing user value measurement dimension is also a problem to be solved.Therefore, based on the RFM model, combined with the characteristics of crowdsourcing platform and crowdsourcing users, this paper firstly incorporates user credit into the user value model, proposes and constructes a crowdsourcing user value measurement model-RFMC.Secondly, combined with the required data obtained on the platform of “Yipinweike”, using GBDT algorithm to complete the crowdsourcing user classification.Finally, the classification performance of Nave Bayes, Multinomial Logistic Regression and GBDT are compared.Also, the classification performance of RFMC model is compared with that of traditional model without considering user credit.Evaluation indicators show that the proposed model is suitable for crowdsourcing users and has good experimental results.

Key words: Crowdsourcing, GBDT algorithm, RFM model, User segmentation

CLC Number: 

  • C934
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