收稿日期: 2015-10-25
修回日期: 2015-11-11
网络出版日期: 2015-12-05
Study on the Usefulness Ranking Model of Mobile O2O Online Reviews from the Perspective of User's Information Demand:Taking an Example of Meituan
Received date: 2015-10-25
Revised date: 2015-11-11
Online published: 2015-12-05
[目的/意义] 为了能够准确发现满足用户信息需求的有用在线评论,对在线评论按照有用性进行排序,帮助用户进行消费决策。[方法/过程] 首先通过文献调研和专家咨询,结合移动O2O用户在线评论的特点,选取影响移动O2O在线评论有用性的8项指标,采用模糊层次分析法进行指标赋权,然后利用定量方法进行指标量化,使用加权灰色关联分析方法进行有用性计算和排序,构建有用性排序模型。最后获取美团商品的在线评论数据进行应用研究,采用与美团原始评论排序结果对比分析的方法,检验方法的可行性和科学性。[结果/结论] 实验结果表明,采用移动O2O在线评论有用性排序模型进行排序,排名靠前的在线评论信息量大,包含产品或服务的维度较多,能够较好地满足用户的个性化信息需求,为用户消费决策提供更大的参考价值。
郭顺利 , 张向先 , 李中梅 . 面向用户信息需求的移动O2O在线评论有用性排序模型研究——以美团为例[J]. 图书情报工作, 2015 , 59(23) : 85 -93 . DOI: 10.13266/j.issn.0252-3116.2015.23.013
[Purpose/significance] In order to accurately find out the useful online comments to meet users' information needs, this paper ranks the users' online reviews in accordance with the usefulness and helps users make consumption decisions. [Method/process] Firstly, through the literature research and expert consultation, combined with the characteristics of mobile O2O users online reviews, this paper selects 8 indicators which affect the usefulness of mobile O2O online reviews, and weights them with the fuzzy AHP method. Then it quantifies them with the quantitative method, calculates and sorts them through the weighted gray correlation analysis method, and builds a usefulness ranking model. Finally, the online reviews data of Meituan products are collected and compared with the ranking results of Meituan original reviews, to verify the feasibility and the scientific nature of the method.[Result/conclusion] The experimental results show that the top online reiews ranked with the usefulness ranking model of mobile O2O online reviews, contain large amount of information and refer to multiple dimensions of products or services, which can better meet the users' personalized information needs and provide with greater reference value for consumer decisions.
[1] 中国电子商务研究中心.2014年度中国电子商务市场数据检测报告[EB/OL].[2015-04-08].http://www.100ec.cn/zt/upload_data/20150408.pdf.
[2] Utz S, Kerkhof P, van den Bos J. Consumers rule:How consumer reviews influence perceived trustworthiness of online stores[J]. Electronic Commerce Research and Applications, 2012, 11(1):49-58.
[3] Mudambi S M, Schuff D.What makes a helpful online review? A study of customer reviews on Amazon.com[J].MIS Quarterly,2010,34(1):185-200.
[4] 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(8):78-88,96.
[5] 严建援,张丽,张蕾.电子商务中在线评论内容对评论有用性影响的实证研究[J].情报科学,2012(5):713-716,719.
[6] 廖成林,蔡春江,李忆.电子商务中在线评论有用性影响因素实证研究[J].软科学,2013(5):46-50.
[7] Liu Y, Huang X, An A, et al. Modeling and predicting the helpfulness of online reviews[C]//Proceedings of the 8th IEEE International Conference on Web Mining. Piscatway:IEEE,2008:443-452.
[8] 殷国鹏,刘雯雯,祝珊.网络社区在线评论有用性影响模型研究——基于信息采纳与社会网络视角[J].图书情报工作,2012,56(16):140-147.
[9] 姜巍,张莉,戴翼,等.面向用户需求获取的在线评论有用性分析[J].计算机学报,2013(1):119-131.
[10] 金丽君.基于SVM的搜索型商品评论有用性自动识别方法研究[D].哈尔滨:哈尔滨工业大学,2013.
[11] 郭国庆,陈凯.消费者在线评论可信度的影响因素研究[J].当代经济管理,2010,10(32):17-23.
[12] 王倩倩.一种在线商品评论信息可信度的排序方法[J].情报杂志,2015(3):181-185.
[13] 张林,钱冠群,樊卫国,等.轻型评论的情感分析研究[J].软件学报,2014(12):2790-2807.
[14] 彭岚,周启海,邱江涛.消费者在线评论有用性影响因素模型研究[J].计算机科学,2011(8):205-207,244.
[15] 李志宇. 在线商品评论效用排序模型研究[J].现代图书情报技术,2013(4):62-68.
[16] 林先杰. 在线评论有用性影响因素研究[D].广州:华南理工大学,2014.
[17] 龚思兰,丁晟春,周夏伟,等. 在线商品评论信息可信度影响因素实证研究[J].情报杂志,2013(11):202-207,180.
[18] GooSeeker.MetaSeeker[EB/OL].[2015-09-05].http://www.gooseeker.com/product.
[19] 武汉大学ROST虚拟学习团队.ROST CM6[EB/OL].[2015-03-05].http://www.fanpq.com/soft/uploadsoft/ROSTCM6.rar.
[20] 王聪,刘启华,曹宇,等.融合情境的移动美食推荐系统研究[J].电脑知识与技术,2015(11):255-260.
[21] 数据堂.台湾大学NTUSD简体中文情感极性词典[EB/OL].[2015-09-05].http://www.datatang.com/data/11837.
[22] 张吉军. 模糊一致判断矩阵3种排序方法的比较研究[J].系统工程与电子技术,2003, 25(11):1370-1372.
[23] 张吉军. 模糊层次分析法(FAHP)[J].模糊系统与数学,2000,14(2):80-88.
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