众包平台用户价值识别与细分:基于改进的RFM模型

计算机科学 ›› 2022, Vol. 49 ›› Issue (4): 37-42.doi: 10.11896/jsjkx.210800255

• 基于社会计算的多学科交叉融合专题^* • 上一篇下一篇

众包平台用户价值识别与细分:基于改进的RFM模型

陈丹红, 彭张林, 万德全, 杨善林

合肥工业大学管理学院合肥 230009; 过程优化与智能决策教育部重点实验室合肥 230009

收稿日期:2021-08-30 修回日期:2021-12-09 发布日期:2022-04-01
通讯作者: 彭张林(pengzhanglin@hfut.edu.cn.)
作者简介:(alicealice_hi@163.com)
基金资助:
教育部人文社会科学研究基金(16YJC630093); 国家自然科学基金(72071060,71601066,71901086)

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

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

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).

摘要/Abstract

摘要： 在众包平台上,不同类型的用户在参与意愿、工作动机、业务能力等方面具有多样性和差异性的特征,在平台上产生的价值也不同。基于用户价值度量对用户进行细分,是更好地洞察用户价值和需求、对用户进行个性化和精细化管理的关键。同时,选择众包用户价值衡量维度也是目前需要解决的问题。因此,该研究首先基于RFM模型并结合众包平台及众包用户的特性,将用户信用纳入用户价值模型,提出并构建了众包用户价值衡量模型RFMC(Recency,Frequency,Monetary,Credit);然后,结合“一品威客”平台获取所需的实验数据,运用GBDT算法完成众包用户分类;最后,比较了Nave Bayes,Multinomial Logistic Regression与GBDT算法的分类效果,并比较了不考虑用户信用的传统模型与RFMC模型的分类效果。结果表明,所提模型适用于众包用户且具有较好的实验效果。

关键词: GBDT算法, RFM模型, 用户细分, 众包

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

中图分类号:

C934

陈丹红, 彭张林, 万德全, 杨善林. 众包平台用户价值识别与细分:基于改进的RFM模型[J]. 计算机科学, 2022, 49(4): 37-42. https://doi.org/10.11896/jsjkx.210800255

CHEN Dan-hong, PENG Zhang-lin, WAN De-quan, YANG Shan-lin. Identification and Segmentation of User Value in Crowdsourcing Platforms:An Improved RFMModel[J]. Computer Science, 2022, 49(4): 37-42. https://doi.org/10.11896/jsjkx.210800255

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