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New Technology of Library and Information Service  2015, Vol. 31 Issue (7-8): 24-30    DOI: 10.11925/infotech.1003-3513.2015.07.04
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Exploring the Co-recommendation Relationship and Its Core Structure Features of Academic Blogs——Taking ScienceNet.cn Blog as an Example
Tan Min1, Xu Xin2, Zhao Xing2
1 Department of Information Resource Management, Zhejiang University, Hangzhou 310027, China;
2 Department of Information Science, Business School, East China Normal University, Shanghai 200241, China
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Abstract  

[Objective] Try to combine information recommendation and co-occurrence into a new informational relation, namely information co-recommendation in online academic blogs. [Methods] Taking ScienceNet.cn Blog as an example, use network analysis as the basis of quantitative analysis to explore the features of co-recommendation in academic blogs. [Results] The empirical research of ScienceNet.cn Blog shows that compared to the other types of networks, the case has the structural characteristics of high cohesiveness, active interaction and balanced strength; the network takes node group as the network core, and the relative balance occurs in the core group. [Limitations] Co-recommendations have different motivations and functions in different application fields. However, this paper only gives an empirical research on ScienceNet.cn. [Conclusions] The co-recommendation can be an option for future studies of academic blogs. This behavior presents more equality in the structure.

Received: 02 March 2015      Published: 25 August 2015
:  G203  

Cite this article:

Tan Min, Xu Xin, Zhao Xing . Exploring the Co-recommendation Relationship and Its Core Structure Features of Academic Blogs——Taking ScienceNet.cn Blog as an Example. New Technology of Library and Information Service, 2015, 31(7-8): 24-30.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2015.07.04     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2015/V31/I7-8/24

[1] Newman M E J. Networks: An Introduction [M]. Oxford: Oxford University Press, 2010.
[2] Clauset A. Finding Local Community Structure in Networks [J]. Physical Review E, 2005, 72(2): No.026132.
[3] Adomavicius G, Tuzhilin A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions [J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
[4] Lü L, Medo M, Yeung C H, et al. Recommender Systems [J]. Physics Reports-Review Section of Physics Letters, 2012, 519(1): 1-49.
[5] Pathak B, Garfinkel R S, Gopal R D, et al. Empirical Analysis of the Impact of Recommender Systems on Sales [J]. Journal of Management Information Systems, 2010, 27(2): 159-188.
[6] Small H G. Relationship Between Citation Indexing and Word Indexing—Study of Co-occurrences of Title Words and Cited References [J]. Proceedings of the American Society for Information Science, 1973, 10: 217-218.
[7] Van Rijsbergen C J. A Theoretical Basis for the Use of Co-occurrence Data in Information Retrieval [J]. Journal of Documentation, 1977, 33(2): 106-119.
[8] Zhang J, Jastram I. A Study of Metadata Element Co-occurrence [J]. Online Information Review, 2006, 30(4): 428-453.
[9] Su H N, Lee P C. Mapping Knowledge Structure by Keyword Co-occurrence: A First Look at Journal Papers in Technology Foresight [J]. Scientometrics, 2010, 85(1): 65-79.
[10] 邱均平, 王菲菲. 基于共现与耦合的馆藏文献资源深度聚合研究探析[J]. 中国图书馆学报, 2013, 39(3): 25-33. (Qiu Junping, Wang Feifei. An Exploration of In-depth Aggregation of Library Document Resources Based on Co-occurrence and Coupling [J]. Journal of Library Science in China, 2013, 39(3): 25-33.)
[11] Lou W, Qiu J P. Semantic Information Retrieval Research Based on Co-occurrence Analysis [J]. Online Information Review, 2014, 38(1): 4-23.
[12] Egghe L, Rousseau R. Co-citation, Bibliographic Coupling and a Characterization of Lattice Citation Networks [J]. Scientometrics, 2002, 55(3): 349-361.
[13] Yan E, Ding Y. Scholarly Network Similarities: How Bibliographic Coupling Networks, Citation Networks, Cocitation Networks, Topical Networks, Coauthorship Networks, and Coword Networks Relate to Each Other [J]. Journal of the American Society for Information Science and Technology, 2012, 63(7): 1313-1326.
[14] Zhao S X, Ye F Y. Power-law Link Strength Distribution in Paper Cocitation Networks [J]. Journal of the American Society for Information Science and Technology, 2013, 64(7): 1480-1489.
[15] Zhao S X, Ye F Y. Exploring the Directed H-degree in Directed Weighted Networks [J]. Journal of Informetrics, 2012, 6(4): 619-630.
[16] 徐孝娟, 赵宇翔, 朱庆华. 民族志决策树方法在学术博客用户行为中的研究——以科学网博客为例[J]. 现代图书情报技术, 2014(1): 79-86. (Xu Xiaojuan, Zhao Yuxiang, Zhu Qinghua. Explore User's Behavior of Academic Blog Based on EDTM: Take Blog.Sciencenet as an Example [J]. New Technology of Library and Information Service, 2014(1): 79-86.)
[17] 周春雷, 朱向林. 科学网图情博客发展现状研究[J]. 图书情报知识, 2013(5): 98-105. (Zhou Chunlei, Zhu Xianglin. Study on LIS Blogs in Science Net [J]. Document, Informaiton & Knowledge, 2013(5): 98-105.)
[18] Borgatti S P, Everett M G. Everett, Models of Core/Periphery Structures [J]. Social Networks, 1999, 21(4): 375-395.
[19] Zhao S X, Zhang P L, Li J, et al. Abstracting the Core Subnet of Weighted Networks Based on Link Strengths [J]. Journal of the Association for Information Science and Technology, 2014, 65(5): 984-994.
[20] Zelnio R. Identifying the Global Core-Periphery Structure of Science[J]. Scientometrics, 2012, 91(2): 601-615.
[21] Amrit C, van Hillegersberg J. Exploring the Impact of Socio-Technical Core-Periphery Structures in Open Source Software Development [J]. Journal of Information Technology, 2010, 25(2): 216-229.
[22] Newman M E J. The Structure and Function of Complex Networks [J]. SIAM Review, 2003, 45(2): 167-256.
[23] 邹文篪, 田青, 刘佳. "投桃报李"——互惠理论的组织行为学研究述评[J]. 心理科学进展, 2012, 20(11): 1879-1888. (Zou Wenchi, Tian Qing, Liu Jia. "Give a Plum in Return for a Peach": A Review of Reciprocity Theory of Organizational Behavior [J]. Advances in Psychological Science, 2012, 20(11): 1879-1888.)
[24] 叶鹰, 张力, 赵星, 等. 用共关键词网络揭示领域知识结构的实验研究[J]. 情报学报, 2012, 31(12): 1245-1251. (Ye F Y, Zhang P L, Zhao S X, et al. An Experimental Study on Revealing Domain Knowledge Structure by Co- keyword Networks [J]. Journal of the China Society for Scientific and Technical Information, 2012, 31(12): 1245-1251.)

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