基于在线评级和评论的评价者效用机制研究

doi:10.16381/j.cnki.issn1003-207x.2016.05.017

中国管理科学 ›› 2016, Vol. 24 ›› Issue (5): 149-157.doi: 10.16381/j.cnki.issn1003-207x.2016.05.017

基于在线评级和评论的评价者效用机制研究

施晓菁¹, 梁循¹, 孙晓蕾²

1. 中国人民大学信息学院, 北京 100872;
2. 中科院科技政策与管理科学研究所 100190

收稿日期:2014-06-27 修回日期:2014-12-21 出版日期:2016-05-20 发布日期:2016-05-24
通讯作者: 梁循(1965-),男(汉族),北京人,中国人民大学信息学院教授,研究方向:数据挖掘、网络金融、社会计算,E-mail:xliang@ruc.edu.cn. E-mail:xliang@ruc.edu.cn
基金资助:
国家自然科学基金资助项目(71531012,71271211);京东商城电子商务研究项目(413313012);北京市自然科学基金资助项目(4132067);中国人民大学品牌计划项目(10XNI029)

Rater Utility Mechanism Research Based On Online Rating and Comment

SHI Xiao-jing¹, LIANG Xun¹, SUN Xiao-lei²

1. School of Information, Renmin University of China, Beijing 100872, China;
2. Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China

Received:2014-06-27 Revised:2014-12-21 Online:2016-05-20 Published:2016-05-24

摘要/Abstract

摘要： 互联网中对产品和服务的评价越来越受到重视,因为评价能够消除消费者的不确定性,辅助其做出购买决策。大多数在线购物网站中用户的评价包括评级和评论。现有的评价反馈系统和评价研究往往只单独关注评价者之间的评级或评论,而忽略了两者之间的有机统一。评价者的评级并不一定反映评价者的真实评价,很多评价者更倾向于选择评论文本来表达自己的真实情感。本文以从淘宝网抓取的852071条评价数据为基础,通过分析评价者评级和评论之间的不一致性,结合RFM模型,考虑评级和评论两种信息形成的评价效用,提出了RFMA模型来计算评价者的总体评价效用。并据此对好的与不好的评价者进行区分,进一步为消费者的购买决策提供支持。通过对评价者的总体分析可以得出,本文提出的机制更加具有可用性和有效性。

关键词: 评级和评论的不一致性, 评价, 评价者, RFMA

Abstract: Appraisals for products and services are increasingly important on the Internet, as they eliminate consumers' uncertainty, and help them to make purchase decision.Raters' appraisals for products are divided into ratings and comments in most online shopping sites.The existing online reputation system and appraisal studies tend to focus on the user rating or comment respectively, but ignore the organic unification between them.User ratings do not fully reflect users' real evaluation, as they are inclined to express their true feelings by comments.On the basis of the 852071 appraisal captured from Taobao, this paper proposes RFMA model to calculate raters' appraise quality, which combines RFM model and considers two kinds of information containing rating and comment by analyzing the inconsistency of rating and comment.Then the good raters and bad raters are distinguished, and further support for consumer purchase is provided.The proposed RFMA model finds a new mechanism for measuring raters' effectiveness.It can be used as a basement for shopping platform to classify the raters, and provide a new way of thinking to further improve the existing online reputation system.Through analyzing all of the raters, it can be concluded that the mechanism of combining the comments is more available and effective.

Key words: inconsistency of rating and comment, appraise, rater, RFMA

中图分类号:

F272.5

施晓菁, 梁循, 孙晓蕾. 基于在线评级和评论的评价者效用机制研究[J]. 中国管理科学, 2016, 24(5): 149-157.

SHI Xiao-jing, LIANG Xun, SUN Xiao-lei. Rater Utility Mechanism Research Based On Online Rating and Comment[J]. Chinese Journal of Management Science, 2016, 24(5): 149-157.

参考文献

[1] Vavilis S, Petkovi? M, Zannone N.A reference model for reputation systems[J].Decision Support Systems, 2014, 61:147-154.

[2] Jøsang A, Ismail R, Boyd C.A survey of trust and reputation systems for online service provision[J].Decision support systems, 2007, 43(2):618-644.

[3] Park D H, Lee J, Han I.The effect of on-line consumer reviews on consumer purchasing intention:The moderating role of involvement[J].International Journal of Electronic Commerce, 2007, 11(4):125-148.

[4] Chen Mao, Singh J P.Computing and using reputations for internet ratings[C]//Proceedings of the 3rd ACM conference on Electronic Commerce.Tampa,Florida,USA,October 14-17,2001.

[5] Dellarocas C.The digitization of word of mouth:Promise and challenges of online feedback mechanisms[J].Management science, 2003, 49(10):1407-1424.

[6] 郝媛媛, 叶强, 李一军.基于影评数据的在线评论有用性影响因素研究[J].管理科学学报, 2010, 13(8):78-88.

[7] Harrison-Walker L J.The measurement of word-of-mouth communication and an investigation of service quality and customer commitment as potential antecedents[J].Journal of Service Research, 2001, 4(1):60-75.

[8] 杨铭, 祁巍, 闫相斌.在线商品评论的效用分析研究[J].管理科学学报, 2012, 15(5):65-75.

[9] Ghose A, Ipeirotis P G.Designing novel review ranking systems:Predicting the usefulness and impact of reviews[C]//Proceedings of the ninth international conference on Electronic commerce,Minneapolis,MN,USA,August 19-22,2007.

[10] 蔡淑琴, 马玉涛, 王瑞.在线口碑传播的意见领袖识别方法研究[J].中国管理科学, 2013, 21(2):185-192.

[11] Rabelo J C B, Prudêncio R C B, Barros F A.Leveraging relationships in social networks for sentiment analysis[C]//Proceedings of the 18th Brazilian symposium on Multimedia and the Web,São Paulo,Brazil,October 15-18,2012.

[12] Shen Yang, Li Shuchen, Zheng Ling, et al.Emotion mining research on micro-blog[C]//Proceedings of 1st IEEE Symposium on Web Society,Lanzhou,China,August 23-24,2009.

[13] 刘群,李素建.基于知网的词汇语义相似度的计算[C]//第三届汉语词汇语义学研讨会,台北,2002.

[14] Brin S, Page L.The anatomy of a large-scale hypertextual Web search engine[J].Computer networks and ISDN systems, 1998, 30(1):107-117.

[15] Chen Mao, Singh J P.Computing and using reputations for internet ratings[C]//Proceedings of the 3rd ACM conference on Electronic Commerce,Tampa,Florida,USA,October 14-17,2001.

[16] Breslow N E, Day N E.Statistical methods in cancer research[M].Lyon:International Agency for Research on Cancer, 1987.

[17] Hu Nan, Pavlou P A, Zhang Jie.Can online reviews reveal a product's true quality?:Empirical findings and analytical modeling of Online word-of-mouth communication[C]//Proceedings of the 7th ACM conference on Electronic Commerce,Ann Arbor,Michigan,USA,June 11-15,2006.

[18] Blei D M, Ng A Y, Jordan M I.Latent dirichlet allocation[J].the Journal of machine Learning research, 2003, 3:993-1022.

[19] Hughes AM.Boosting response with RFM[J].American Demographics, 1996, 5:4-9.

[20] Stone B, Jacobs R.Successful direct marketing methods[M].Lincolnwood, IL:NTC Business Books, 1988.

[21] Ghose A, Ipeirotis P G.Designing novel review ranking systems:Predicting the usefulness and impact of reviews[C]//Proceedings of the ninth international conference on Electronic commerce,Minneapolis,MN,USA,August 19-22,2007.

[22] Ou C X, Davison R M.Technical opinion Why eBay lost to TaoBao in China:The glocal advantage[J].Communications of the ACM, 2009, 52(1):145-148.

基于在线评级和评论的评价者效用机制研究

Rater Utility Mechanism Research Based On Online Rating and Comment

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