收稿日期: 2016-03-29
修回日期: 2016-05-24
网络出版日期: 2016-06-05
基金资助
本文系教育部人文社会科学研究规划基金项目“基于后结构主义网络分析的Folksonomy模式中社群知识非线性自组织研究”（项目编号：14YJA870010）和国家自然科学基金项目“基于网络结构演化的Folksonomy模式中社群知识组织与知识涌现研究”（项目编号：71473035）研究成果之一。
Research on Preference Relation Between Users and Tags Based on Correspondence Analysis
Received date: 2016-03-29
Revised date: 2016-05-24
Online published: 2016-06-05
［目的/意义］用户认知能够影响客观知识之间潜在的关联关系，研究Folksonomy模式中用户与标签之间的偏好关系有助于揭示认知与知识之间的互动关系及其模式规律，并为人文因素影响下的知识组织提供参考。［方法/过程］基于标签与用户的隶属关系构建“标签-用户”2-模网络，采用多变量的对应分析方法，基于联合空间距离位置对用户与标签间的偏好关系进行分析。［结果/结论］研究表明，用户与标签的偏好关系与联合空间中的位置和2-模网络的中心度具有相关性，在偏好问题上不会有群体极化现象发生，群体决策的科学性得以保障。
关键词： Folksonomy; 偏好关系; 对应分析; 复杂网络
滕广青, 陈思, 常志远, 姜博, 刘雅姝, 赵汝南, 张利彪 . 基于对应分析的用户与标签间偏好关系研究[J]. 图书情报工作, 2016 , 60(11) : 120 -127 . DOI: 10.13266/j.issn.0252-3116.2016.11.017
[Purpose/significance] Users' cognition can influence the potential relationship between objective knowledge. The research on preference relation between users and tags is helpful to reveal the relationship and rule mode of interaction between cognition and knowledge, and provide a reference for knowledge organization under the human impact. [Method/process] The "tag-user" 2-mode network is constructed based on affiliation of tags and users.With multivariate correspondence analysis method, the preference relations between users and tags are analyzed based on distance and position in joint space. [Result/conclusion] The results show that the preference relations between users and tags are related with the position in joint space and centrality in 2-mode network, there is no the phenomenon of group polarization about preference, the scientificity of group decision-making can be guaranteed.
Key words： Folksonomy; preference relation; correspondence analysis; complex network
[1] WEINBERGER D. 新数字秩序的革命[M]. 张岩, 译. 北京: 中信出版社, 2008.
[2] POPPER K. 客观的知识:一个进化论的研究[M]. 舒炜光, 卓如飞, 梁咏新,等译. 杭州: 中国美术学院出版社, 2003: 109-153.
[3] TONKIN E. Searching the long tail: hidden structure in social tagging[J]. Advances in classification research online, 2006, 17(1): 1-10.
[4] SHEPITSEN A, GEMMELL J, MOBASHER B, et al. Personalized recommendation in social tagging systems[C]//PU P, BRIDGE D, MOBASHER B, et al.Proceedings of the 2008 ACM conference on recommender systems. New York: ACM, 2008: 259-266.
[5] UDDIN M N, DUONG T H, NGUYEN N T, et al. Semantic similarity measures for enhancing information retrieval in folksonomies[J]. Expert systems with applications, 2013, 40(5): 1645-1653.
[6] LEWIS T G. 网络科学:原理与应用[M]. 陈向阳, 巨修炼,等译. 北京: 机械工业出版社, 2011: 1-14.
[7] HEYMANN P, GARCIA-MOLINA H. Collaborative creation of communal hierarchical taxonomies in social tagging systems[EB/OL]. [2016-03-15]. http://ilpubs.stanford.edu:8090/775/1/2006-10.pdf.
[8] CATTUTO C. Emergent community structure in social tagging systems[J]. Advances in complex systems, 2008, 11(4): 597-608.
[9] RUSSELL T. Contextual authority tagging: cognitive authority through folksonomy[EB/OL]. [2016-03-08]. http://www.terrellrussell.com/projects/contextualauthortytagging/conauthtag200505.pdf.
[10] GLEICK J. 信息简史[M]. 高博, 译. 北京: 人民邮电出版社, 2013: 265-279.
[11] PETERS I, STOCK W G. Folksonomy and information retrieval[J]. Proceedings of the American Society for Information Science and Technology, 2007, 44(1): 1-28.
[12] MATHES A. Folksonomies-cooperative classification and communication through shared meadata[EB/OL]. [2016-03-08]. http://adammathes.com/academic/computer-mediated-communication/folksonomies.pdf.
[13] MAS M D. Intelligent interface architectures for folksonomy driven structure network[C]// BAROLLI L, XHAFA F, VITABILE S, et al.Proceeding of the sixth international conference on Complex, intelligent and software intensive systems. Washington DC: IEEE Computer Society, 2012: 519-525.
[14] 熊回香, 王学东. 大众分类体系中标签与本体的映射研究[J]. 情报科学, 2014, 32(3): 121-126.
[15] 邰杨芳, 贾君枝, 贺培风. 基于受控词表的Folksonomy优化系统分析与设计[J]. 情报科学, 2014, 32(2): 112-117.
[16] 张艳桃, 王国胤, 于洪. 面向Folksonomy的用户兴趣相似性度量方法[J]. 南京大学学报(自然科学), 2013, 49(5): 588-595.
[17] 滕广青, 贺德方,彭洁,等. 基于"用户-标签"关系的社群知识自组织研究[J]. 图书情报工作, 2014, 58(20): 106-111.
[18] 邓三鸿, 刘齐进, 夏立新. 博物馆中面向图像需求表达的标签分类研究[J]. 图书情报工作, 2016, 60(2): 14-21.
[19] HU C, RACHERLA P. Visual representation of knowledge networks: a social network analysis of hospitality research domain[J]. International journal of hospitality management, 2008, 27(2): 302-312.
[20] 滕广青, 姜航. 社群用户信息决策中级联效应判识的修正[J]. 情报科学, 2013, 31(1): 106-110, 117.
[21] SUROWIECKI J. 群体的智慧:如何做出最聪明的决策[M]. 王宝泉, 译. 北京: 中信出版社, 2010: 197-215.
[22] FISHER L. 完美的群体:如何掌控群体智慧的力量[M]. 邓逗逗, 译. 杭州: 浙江人民出版社, 2013: 13-34.
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