社会化标注系统中个性化信息推荐模型研究
|
摘要
在Web3.0时代，人们不再满足由机器挖掘给用户的各种信息，而是要结合自身的偏好享用个性化的信息服务，无论是产品还是服务，都将为每一用户量身打造，信息世界将变得越来越聪明和智能，似乎比用户还了解他想要的是什么，这就是个性化的信息服务，即Web3.0的内涵。所以，如何在浩如烟海的信息中寻求个性化的信息，就是目前学术界需要解决的热点研究问题。同时，在另一方面，随着Internet网络的产生和使用，用户在社会化标注系统(Web页面)上产生了大量的代表自己个性化信息的数据，那么如何充分利用用户产生的数据来解决目前的个性化信息服务问题，就成了一个情报学术界研究的热点学术问题。
社会化标注系统主要是以“用户-资源-标签”三元关系为研究对象的典型复杂动态网络，用户可以根据个人的需要自由选择词汇对所喜爱的资源进行标注，每添加一个词汇被称为对资源添加一个标签，用户、资源和标签组成了社会化标注系统的三个基本元素。从这三个最基本的组成元素开始，从中提炼用户的个性化信息，进而形成个性化信息推荐，成为论文的研究起点。
本文针对社会化标注系统中的个性化信息推荐模型问题展开研究，在综述“社会化标注系统”、“个性化信息推荐”的基础上，对国内外“社会化标注系统中的个性化信息推荐模型”研究现状进行分析，通过自组织理论、社会网络分析理论、系统动力学等理论与方法围绕社会化标注系统的演化过程、用户关系网络、个性化信息推荐模型构建等核心问题进行详细分析。
本文研究的内容主要分为五大部分：
内容一：研究目前国内外社会化标注系统、个性化信息推荐两方面的研究进展，分析研究的热点与前沿问题，掌握在具体的研究中其所面临的不足，然后从不足处展开，形成本文所要研究的起点，即利用社会网络分析研究用户关系网络，作为分析个性化信息推荐的逻辑起点。
内容二：研究社会化标注系统的演化形式及耗散结构。
对社会化标注系统中的用户、资源、标签及三者之间的关系进行分析，利用自组织理论研究社会化标注系统的演化机理，具体使用超循环和耗散结构分别分析系统中的用户、标签、资源各自的演化形式及系统的多层级耗散结构。
内容三：分析社会化标注系统中的用户关系网络。
主要分析了用户关系网络的结构特征，如网络密度、核心-边缘结构、中心性等，同时，从用户关系网络中挖掘凝聚子群的各种信息偏好，从两个角度：群内和群际对信息进行分析，发现个性化的信息。
内容四：构建社会化标注系统中的个性化信息推荐模型。
从静态、动态两个角度分别构建社会化标注系统中的个性化信息推荐模型；然后利用系统动力学绘制个性化信息推荐的系统模型，主要由系统的因果关系和系统流图两个步骤构成。
内容五：对个性化信息推荐进行实证研究，首先从豆瓣网中抓取数据，然后对其进行自组织演化、用户关系网络结构分析、个性化信息推荐模型构建等。
本文的创新点体现在如下两个方面：
第一，研究视角的创新。
本文从社会网络的角度研究在用户标注行为中产生的用户关系网络，从中提取个性化信息，并从群内和群际两个角度进行个性化信息推荐分析。当前的相关研究主要局限于社会化标注系统的网络结构分析，少部分研究涉及到了凝聚子群，群内和群际信息推荐的研究成果几乎没有。因此，本文为个性化信息推荐问题的研究引入了全新的分析视角。
第二，研究内容的创新。
本文采用“移植”借鉴的方法，通过科学“移植”信息科学领域的经典理论，对社会化标注系统中的个性化信息推荐模型进行深入研究，创新认识，具体体现在：根据自组织理论提出了社会化标注系统的超循环演化形式和多层级耗散结构，深化了系统演化的过程，为模型建立打下了深厚的理论基础；根据社会网络分析理论提出了基于群内的个性化信息推荐和基于群外的个性化信息推荐两种推荐方法，深化了方法论思想；构建了个性化信息推荐模型的静态和动态模型，并运用系统动力学对个性化信息推荐模型进行动力学影响因素分析，提高了模型化的程度。
本文的研究结论具体如下：
第一，基于自组织理论的社会化标注系统的演化形式遵循超循环理论，同时具有层级耗散结构特征。社会化标注系统具有自组织系统的开放性、远离平衡态、非线性相关性、随机涨落等特征；然后运用超循环理论分别对用户集、资源集、标签集及社会化标注系统的自组织演化机理进行了探讨，并构建了社会化标注系统的自组织演化模型；最后根据耗散结构理论，从多层级的角度，对社会化标注系统的序化形成过程进行研究，提出了系统序化的平衡极点。
第二，从社会网络分析的角度，可以发现社会化标注系统的用户关系网络结构和凝聚子群。主要分析了用于关系网络的网络密度、核心-边缘结构、中心性等，还分析了用户关系网络的凝聚子群，利用块模型的“结构对等性”对行动者进行聚类，利用K-核分析对块模型进行补充，利用结构洞方法发现存在结构洞的用户，即基于群际的用户。
第三，社会化标注系统中的个性化信息推荐模型可以分为静态模型和动态模型来构建。首先从系统学的角度研究构成个性化信息推荐系统的六大要素；然后在遵循构建概念的原则下，构建了个性化信息推荐的静态模型和动态模型；最后利用系统动力学对个性化信息推荐的模型进行影响因素分析，即当网络的密度较小时，基于群内的个性化信息推荐与基于群际的个性化信息推荐受网络密度的影响较大；当网络的密度较大时，基于群内的个性化信息推荐受核心人物的影响较大，基于群际的个性化信息推荐受图的中心势值影响较大。
In the Web3.0era, people no longer satisfy various kinds of informationby machine mining, but to combine its own preference for personalized informationservices. Products and services will be tailored for each user, the informationworld will become more and more intelligent and smart, it seems that theinformation world knows more than the users to understand what he wants, andthis is the personalized information services, namely the connotation of Web3.0. So, how to seek personalized information in the voluminous information ispresently the research hot problems need to be solved. At the same time, on theother hand, with the use of the Internet, users produced a large number of datathat they are on their own individual information data in the social taggingsystem (Web page), so how to make full use of the data generated by the userto solve the problem of the individualized information service, became a hotacademic intelligence of the academic research.
Social tagging system is a typical complex dynamic network that it mainlybased on "user-resources-label" ternary relationship, users can tag theresources of favorite according to the needs of individual, freely choice ofthe resources, each adding a word is called to add a label for resources. Theusers, resources and tags become the three basic elements of social taggingsystem. The paper start the study from the basic composition of the three elements,and extract for the user's personalized information, then personalizedinformation recommendation.
This paper launches the model research of personalized informationrecommendation in social tagging system, analyzing the present situation andproblems of research on the basis of the domestic and foreign personalizedinformation recommendation model,based the review of the "social taggingsystem" and "personalized information recommendation". The paper analyzes the processes of the social tagging system, user relationship network structure,personalized information recommendation model building through theself-organization theory, social network analysis theory, and system dynamicstheory.
This paper studies the main content is divided into five:
Content one: The paper studies the progress of social tagging systems andpersonalized information recommendation at home and abroad, and analyzes thehot and frontier issues, masters the shortage in the study, and then formed thestarting point of the study in this paper, beginning from the insufficient place,using the social network analysis to study the user network. This is the logicalstarting point of analysis of personalized information recommendation.
Content two: The paper studies the evolution forms and dissipative structureof social tagging system.
It analyzes the there relations in the social tagging system, and studiesthe evolution of the social tagging system mechanism, using theself-organization theory.they are the super cycle and the dissipative structure,and this paper analyzes the evolution forms in system user, tag, resources andthe system’s multi-level dissipative structure.
Content three: The paper studies the use network in the social taggingsystem.
It Mainly analyzes the structure characteristics of the users network, suchas network density, the core-edge, centricity, etc., at the same time,subgrouping the preference from the user network, to analyze the informationfrom two perspectives: group and intergroup, and find per sonalized information.
Content four: The paper builds a model of personalized informationrecommendation in the social tagging system.
It sets up the model of personalized information recommendation in the socialtagging system from two aspects: static and dynamic respectively, and then drawsthe personalized information recommendation system using the system dynamics model, mainly consists of two graphics: the system of cause and effectrelationship, the system flow chart of two steps.
Content five: The paper makes the empirical research of personalizedinformation recommendation. Firstly it fetches all data from Dou Ban, thenanalyzes the system self-organization evolution, the network structure of usernetwork, and builds the model of personalized information recommendation.
This paper’s innovations embodied in two aspects:
Firstly,the innovation of research angle.
This paper studies the user network produced the act of user annotationsfrom the perspective of social network, extracting the personalized information,and analysising the personalized information recommendation from two aspects:group and intergroup. The current related research mainly is limited in thesocial tagging system’s network structure analysis, a few studies involvingcondensing subgroup,and hardly studies in the same group and intergroupinformation recommended. Therefore, this paper introduced the new perspectivein the research of personalized information recommendation problem.
Secondly,the innovation of research content.
This paper studies the model of personalized information recommendation inthe social tagging system in-depth, adopting the method of "transplant", throughscientific "transplant" the classic theory in the field of information science,specific include:according to the theory of self-organization, it analyzes theevolution of social tagging system in detail，to deepen the process of systemevolution and lay a profound theoretical foundation of establishing a model；according to the theory of social network analysis, it presents tworecommendations based on the group and group outside of the personalizedinformation recommendation，to deepen the thoughts of methodology；it buildsthe static and dynamic model of the personalized information recommendation,and then analyzes the whole dynamic simulation of the model using systemdynamics,to increase the degree of the model.
This paper’s research conclusion is as follows:
Firstly, the evolution form of social tagging system follows the hypercycletheory based on the theory of self-organization, at the same time has thecharacteristics of dissipative structure hierarchy.The social tagging systemhas the self-organizing system of openness, far from equilibrium, nonlinearcorrelation and random fluctuation characteristics; Then discusses theself-organization evolution mechanism of the social tagging system usinghypercycle theory respectively on the user set, resource set, the label set andthe social tagging system; According to the dissipative structure theory, fromthe perspective of a multi-level, studying the ordering of social tagging system,putting forward the system order balance pole.
Secondly, we can find the social tagging system user relationship networkstructure and condensing subgroup from the perspective of social networkanalysis. It mainly analyzes the network of network density, the core-edgestructure, centricity, and condensing subgroup and so on, using the block modelstructure of "equivalence" to clustering of actors, using the K-nuclear analysisto supplement the block model, using the method of structural holes to find thehole structure of users, based on intergroup users.
Thirdly, the personalized information recommendation in the social taggingsystem model can be divided into static model and dynamic model. First of all,it analyzes the six key elements in personalized information recommendationsystem from the angle of systematics; then under the follow the principle ofbuilding concept, it builds a static model and dynamic model of personalizedinformation recommendation; Finaly it builds the system dynamics simulationmodel of personalized information recommendation system analysis. When thenetwork density is small, the personalized information recommendation based onthe group and the personalized information recommendation based on intergroupis strongly influenced by the network density; When the network density is larger,the personalized information recommendation based on the group is greatly influenced by the central figure, the personalized information recommendationbased on intergroup is greatly influenced by figure at the center of the potentialvalue.
社会化标注系统主要是以“用户-资源-标签”三元关系为研究对象的典型复杂动态网络，用户可以根据个人的需要自由选择词汇对所喜爱的资源进行标注，每添加一个词汇被称为对资源添加一个标签，用户、资源和标签组成了社会化标注系统的三个基本元素。从这三个最基本的组成元素开始，从中提炼用户的个性化信息，进而形成个性化信息推荐，成为论文的研究起点。
本文针对社会化标注系统中的个性化信息推荐模型问题展开研究，在综述“社会化标注系统”、“个性化信息推荐”的基础上，对国内外“社会化标注系统中的个性化信息推荐模型”研究现状进行分析，通过自组织理论、社会网络分析理论、系统动力学等理论与方法围绕社会化标注系统的演化过程、用户关系网络、个性化信息推荐模型构建等核心问题进行详细分析。
本文研究的内容主要分为五大部分：
内容一：研究目前国内外社会化标注系统、个性化信息推荐两方面的研究进展，分析研究的热点与前沿问题，掌握在具体的研究中其所面临的不足，然后从不足处展开，形成本文所要研究的起点，即利用社会网络分析研究用户关系网络，作为分析个性化信息推荐的逻辑起点。
内容二：研究社会化标注系统的演化形式及耗散结构。
对社会化标注系统中的用户、资源、标签及三者之间的关系进行分析，利用自组织理论研究社会化标注系统的演化机理，具体使用超循环和耗散结构分别分析系统中的用户、标签、资源各自的演化形式及系统的多层级耗散结构。
内容三：分析社会化标注系统中的用户关系网络。
主要分析了用户关系网络的结构特征，如网络密度、核心-边缘结构、中心性等，同时，从用户关系网络中挖掘凝聚子群的各种信息偏好，从两个角度：群内和群际对信息进行分析，发现个性化的信息。
内容四：构建社会化标注系统中的个性化信息推荐模型。
从静态、动态两个角度分别构建社会化标注系统中的个性化信息推荐模型；然后利用系统动力学绘制个性化信息推荐的系统模型，主要由系统的因果关系和系统流图两个步骤构成。
内容五：对个性化信息推荐进行实证研究，首先从豆瓣网中抓取数据，然后对其进行自组织演化、用户关系网络结构分析、个性化信息推荐模型构建等。
本文的创新点体现在如下两个方面：
第一，研究视角的创新。
本文从社会网络的角度研究在用户标注行为中产生的用户关系网络，从中提取个性化信息，并从群内和群际两个角度进行个性化信息推荐分析。当前的相关研究主要局限于社会化标注系统的网络结构分析，少部分研究涉及到了凝聚子群，群内和群际信息推荐的研究成果几乎没有。因此，本文为个性化信息推荐问题的研究引入了全新的分析视角。
第二，研究内容的创新。
本文采用“移植”借鉴的方法，通过科学“移植”信息科学领域的经典理论，对社会化标注系统中的个性化信息推荐模型进行深入研究，创新认识，具体体现在：根据自组织理论提出了社会化标注系统的超循环演化形式和多层级耗散结构，深化了系统演化的过程，为模型建立打下了深厚的理论基础；根据社会网络分析理论提出了基于群内的个性化信息推荐和基于群外的个性化信息推荐两种推荐方法，深化了方法论思想；构建了个性化信息推荐模型的静态和动态模型，并运用系统动力学对个性化信息推荐模型进行动力学影响因素分析，提高了模型化的程度。
本文的研究结论具体如下：
第一，基于自组织理论的社会化标注系统的演化形式遵循超循环理论，同时具有层级耗散结构特征。社会化标注系统具有自组织系统的开放性、远离平衡态、非线性相关性、随机涨落等特征；然后运用超循环理论分别对用户集、资源集、标签集及社会化标注系统的自组织演化机理进行了探讨，并构建了社会化标注系统的自组织演化模型；最后根据耗散结构理论，从多层级的角度，对社会化标注系统的序化形成过程进行研究，提出了系统序化的平衡极点。
第二，从社会网络分析的角度，可以发现社会化标注系统的用户关系网络结构和凝聚子群。主要分析了用于关系网络的网络密度、核心-边缘结构、中心性等，还分析了用户关系网络的凝聚子群，利用块模型的“结构对等性”对行动者进行聚类，利用K-核分析对块模型进行补充，利用结构洞方法发现存在结构洞的用户，即基于群际的用户。
第三，社会化标注系统中的个性化信息推荐模型可以分为静态模型和动态模型来构建。首先从系统学的角度研究构成个性化信息推荐系统的六大要素；然后在遵循构建概念的原则下，构建了个性化信息推荐的静态模型和动态模型；最后利用系统动力学对个性化信息推荐的模型进行影响因素分析，即当网络的密度较小时，基于群内的个性化信息推荐与基于群际的个性化信息推荐受网络密度的影响较大；当网络的密度较大时，基于群内的个性化信息推荐受核心人物的影响较大，基于群际的个性化信息推荐受图的中心势值影响较大。
In the Web3.0era, people no longer satisfy various kinds of informationby machine mining, but to combine its own preference for personalized informationservices. Products and services will be tailored for each user, the informationworld will become more and more intelligent and smart, it seems that theinformation world knows more than the users to understand what he wants, andthis is the personalized information services, namely the connotation of Web3.0. So, how to seek personalized information in the voluminous information ispresently the research hot problems need to be solved. At the same time, on theother hand, with the use of the Internet, users produced a large number of datathat they are on their own individual information data in the social taggingsystem (Web page), so how to make full use of the data generated by the userto solve the problem of the individualized information service, became a hotacademic intelligence of the academic research.
Social tagging system is a typical complex dynamic network that it mainlybased on "user-resources-label" ternary relationship, users can tag theresources of favorite according to the needs of individual, freely choice ofthe resources, each adding a word is called to add a label for resources. Theusers, resources and tags become the three basic elements of social taggingsystem. The paper start the study from the basic composition of the three elements,and extract for the user's personalized information, then personalizedinformation recommendation.
This paper launches the model research of personalized informationrecommendation in social tagging system, analyzing the present situation andproblems of research on the basis of the domestic and foreign personalizedinformation recommendation model,based the review of the "social taggingsystem" and "personalized information recommendation". The paper analyzes the processes of the social tagging system, user relationship network structure,personalized information recommendation model building through theself-organization theory, social network analysis theory, and system dynamicstheory.
This paper studies the main content is divided into five:
Content one: The paper studies the progress of social tagging systems andpersonalized information recommendation at home and abroad, and analyzes thehot and frontier issues, masters the shortage in the study, and then formed thestarting point of the study in this paper, beginning from the insufficient place,using the social network analysis to study the user network. This is the logicalstarting point of analysis of personalized information recommendation.
Content two: The paper studies the evolution forms and dissipative structureof social tagging system.
It analyzes the there relations in the social tagging system, and studiesthe evolution of the social tagging system mechanism, using theself-organization theory.they are the super cycle and the dissipative structure,and this paper analyzes the evolution forms in system user, tag, resources andthe system’s multi-level dissipative structure.
Content three: The paper studies the use network in the social taggingsystem.
It Mainly analyzes the structure characteristics of the users network, suchas network density, the core-edge, centricity, etc., at the same time,subgrouping the preference from the user network, to analyze the informationfrom two perspectives: group and intergroup, and find per sonalized information.
Content four: The paper builds a model of personalized informationrecommendation in the social tagging system.
It sets up the model of personalized information recommendation in the socialtagging system from two aspects: static and dynamic respectively, and then drawsthe personalized information recommendation system using the system dynamics model, mainly consists of two graphics: the system of cause and effectrelationship, the system flow chart of two steps.
Content five: The paper makes the empirical research of personalizedinformation recommendation. Firstly it fetches all data from Dou Ban, thenanalyzes the system self-organization evolution, the network structure of usernetwork, and builds the model of personalized information recommendation.
This paper’s innovations embodied in two aspects:
Firstly,the innovation of research angle.
This paper studies the user network produced the act of user annotationsfrom the perspective of social network, extracting the personalized information,and analysising the personalized information recommendation from two aspects:group and intergroup. The current related research mainly is limited in thesocial tagging system’s network structure analysis, a few studies involvingcondensing subgroup,and hardly studies in the same group and intergroupinformation recommended. Therefore, this paper introduced the new perspectivein the research of personalized information recommendation problem.
Secondly,the innovation of research content.
This paper studies the model of personalized information recommendation inthe social tagging system in-depth, adopting the method of "transplant", throughscientific "transplant" the classic theory in the field of information science,specific include:according to the theory of self-organization, it analyzes theevolution of social tagging system in detail，to deepen the process of systemevolution and lay a profound theoretical foundation of establishing a model；according to the theory of social network analysis, it presents tworecommendations based on the group and group outside of the personalizedinformation recommendation，to deepen the thoughts of methodology；it buildsthe static and dynamic model of the personalized information recommendation,and then analyzes the whole dynamic simulation of the model using systemdynamics,to increase the degree of the model.
This paper’s research conclusion is as follows:
Firstly, the evolution form of social tagging system follows the hypercycletheory based on the theory of self-organization, at the same time has thecharacteristics of dissipative structure hierarchy.The social tagging systemhas the self-organizing system of openness, far from equilibrium, nonlinearcorrelation and random fluctuation characteristics; Then discusses theself-organization evolution mechanism of the social tagging system usinghypercycle theory respectively on the user set, resource set, the label set andthe social tagging system; According to the dissipative structure theory, fromthe perspective of a multi-level, studying the ordering of social tagging system,putting forward the system order balance pole.
Secondly, we can find the social tagging system user relationship networkstructure and condensing subgroup from the perspective of social networkanalysis. It mainly analyzes the network of network density, the core-edgestructure, centricity, and condensing subgroup and so on, using the block modelstructure of "equivalence" to clustering of actors, using the K-nuclear analysisto supplement the block model, using the method of structural holes to find thehole structure of users, based on intergroup users.
Thirdly, the personalized information recommendation in the social taggingsystem model can be divided into static model and dynamic model. First of all,it analyzes the six key elements in personalized information recommendationsystem from the angle of systematics; then under the follow the principle ofbuilding concept, it builds a static model and dynamic model of personalizedinformation recommendation; Finaly it builds the system dynamics simulationmodel of personalized information recommendation system analysis. When thenetwork density is small, the personalized information recommendation based onthe group and the personalized information recommendation based on intergroupis strongly influenced by the network density; When the network density is larger,the personalized information recommendation based on the group is greatly influenced by the central figure, the personalized information recommendationbased on intergroup is greatly influenced by figure at the center of the potentialvalue.
引文
[1] Web3.0is about Intelligence[EB/OL].[2008-01-10].http://bub.blicious/?p=432.
[2]谭晨辉,刘青炎.Open API出现、起源与现状[J].程序员,2008(07):38－41.
[3]熊回香,陈珊,许颖颖.基于Web3.0的个性化信息聚合技术研究[J].情报理论与实践,2011.34(8):95－99.
[4]吴汉华,王子舟.从“Web3.0”到“图书馆3.0”[J].图书馆建设,2008(4):66－70.
[5]熊回香,王学东.面向Web3.0的分众分类研究[J].图书情报工作,2010,54(3):104－105.
[6]陈定权,武立斌.社会网络视角下的信息推荐[J].情报杂志,2007(11):37－42.
[7] Furner J. On Recommending[J].Journal of the American Society for InformationScience and Tecnology,2002,53(9):747－763
[8]胡吉明.社会化推荐服务研究述评[J].情报科学,2011,29(2):308－309.
[9]冯蕾,张宇光,唐丽.复杂网络理论在图书馆个性化推荐服务中的应用[J].情报理论与实践,2009,32(2):69－71.
[10] Chiu D KW, Leung H F, Lam K M. On the Making of Service Recommendations: AnAction Theory Based on Utility, Reputation, and Risk Attitude[J].ExpertSystems with Applications,2009,36(2):3293－3301.
[11]魏建良,朱庆华.社会化标注理论研究综述[J].中国图书馆学报,2009,35(184):88－96.
[12] Niwa S, Doi T, Honiden S. Web page recommender system based on folksonomy miningfor ITNG’06submissions[C].Information Technology: New Generations,2006.Third International Conference,2006:388－393.
[13] Sasaki A, Miyata T, Inazumi Y, et al. Web content recommendation system basedon similarities among contents cluster of social bookmark[J].In DB Web2006,2006:59－66.
[14] Michlmayr E, Cayzer S. Learning user profiles from tagging data and leveragingthem form personal(ized) information access[C].In Proceedings of the Workshopon Tagging and Metadata for Social Information Organization,16th InternationalWorld Wide Web Conference,Eigenverlag,2007.
[15] Walter F. E. A model of a trust based recommendation system on a social network[J].Autonomous Agents and Multi Agent Systems,2008,16(1):57－74.
[16] Li Y. M., et al. A synthetical approach for blog Recommendation: Combining trust,social relation, and semantic analysis[J].Expert Systems with Applications,2009,36(3):6536－6547.
[17] http://paper.sciencenet.cn/htmlpaper/201211813464616021710.shtm.2012118/20120301.
[18]任红娟,张志强.基于文献记录的科学知识图谱发展研究[J].情报杂志,2009(12):86－90.
[19] Golder SA, Bernardo A.. Usage patterns of collaborative tagging systems[J].Journal of information science,2006,32(2):198－208.
[20] HothoA, Jasehke R，Sehmi C，etal. Information retrieval in folksonomies: Searchand ranking[EB/OL].http://maps.cs.hut.fi/bib/Folksonomy Ontology/Information_Retrieval_in_Folksonomies__Search_and_Ranking.pdf，2012-04-25.
[21] Marlow, C., Naaman, M., Doyd, Detal. HT06, Tagging Paper, Taxonomy, Flickr,Academic Article, To Read[EB/OL]. http://www.stanford.edu/~koutrika/Readings/res/Default/p31marlow.pdf，2012-04-25.
[22] Mathes, A. Folksonomies Cooperative Classication and Communication ThroughShared Metadata[EB/OL].http://www.citeulike.org/group/862/article/90413，2012-04-25.
[23] Sen S, Shyong K., Al MR,et al.tagging,communities, vocabulary,evolution
[EB/OL].http://folk.uio.no/yennyo/readme/old/p181sen.pdf，2012-04-25.
[24] Ding, Y., E.K. Jacob, Z. Zhang, et al. Perspectives on Social Tagging[J].Journal of the American Society for Information Science and Technology,2009,60(12):2388－2401.
[25] Golder, S., B. A. Huberman,2006,Usage patterns of collaborative taggingsystems[J].Information System.2006,32(2):198－208.
[26] Kim HN, Rawashdeh M, Alghamdi A, et al. Folksonomy based personalized searchand ranking in social media services. Information Systems[J].2011,37(1):61－76.
[27] Kim HN, Roczniak A, Levy P, et al. Social media filtering based on collaborativetagging in semantic space[J].Multimedia Tools and Applications.2012,56(1):63－89.
[28] Millen D., Yang M., Whittaker S.,et al. Social bookmarking and exploratorysearch[EB/OL].http://research.microsoft.com/enus/um/people/ryenw/proceedings/ESI2007.pdf#page=9,2012-04-25.
[29] Belen BM A., Costa ME., Burguillo JC., et al. A hybrid content-based anditem-based collaborative filtering approach to recommend TV programs enhancedwith singular value decomposition[J].Information Sciences.2010,180(22):4290－4311.
[30] Adomavicius G, Tuzhilin A.Toward the next generation of recommender systems:A survey of the state of the art and possible extensions[J].IEEE transactionson knowledge and data engineering,2005,17(6):734－749.
[31] Balabanovic M. Content-Based, Collaborative, Recommendation.1997,40:66－80.
[32] Herlocker JL, Konstan JA, Terveen K, et al. Evaluating collaborative filteringrecommender systems.ACM transactions on information systems.2004,22(1):5－53.
[33] Upendra Shardanand, Pattie Maes. Social information filtering: algorithms forautomating“word of mouth”[J].Proceeding CHI'95Proceedings of the SIGCHIconference on Human factors in computing systems1995：210－217.
[34] Konstan J., Miller B., Maltz, et al.GroupLens: Applying Collaborative Filteringto Usenet News[J].Communications of the ACM,1997,40(3):77－87.
[35] Bonhard P., Sasse M.A. Knowing me, knowing you using profiles and socialnetworking to improve recommender systems[J].Bt Technology Journal,2006.24(3):84－98.
[36] Lambiotte R,Ａusloos M. Collaborative tagging as a tripartite network [EB/OL].
[2012-08-22].http://arxiv.org/pdf/cs/0512090.pdf.
[37] Zhang Zi-Ke, Zhou Tao, Zhang Yi-Cheng. Personalized recommendation viaintegrated diffusion on user-item-tag tripartite graphs[J].Physical,2010,389(1):179－186.
[38] Shang Ming-Sheng, ZhangZi-Ke, Zhou Tao. Collaborative filtering with diffusionbased similarity on tripartite graphs[J].Physical,2010,389(6):1259－1264.
[39] Cattuto C, Schmitz C, Baldassarri A, et al. Network properties of folksonomies[J].AI Communications,2007,20(4):245－262.
[40] Ghoshal G, Zlatic’V, Caldarelli G, et al. Random hypergraphs and theirapplications[EB/OL].[2012-08－22].http://arxiv.org/abs/09030419.
[41] Zlatic’v, Ghoshal G, Caldarelli G. Hypergraphs topological quantities fortagged social networks[EB/OL].[2012-08-22]. http://pre.aps.org/abstract/PRE/v80/i3/e036118.
[42] De Pessemier T, Deryckere T, Martens Luc. Context aware recommendations foruser generated content on a social network site[EB/OL].[2012-08-22].http://www.igiglobal.com/article/ontology folksonomy a shapeless oranges/2828.
[43] Kim HN, El Saddik Abdulmotaleb. Exploring social tagging for personalizedcommunity recommendations[J], USER MODELING AND USERADAPTED INTERACTION,2013,23(2-3):249－285.
[44] Vasanth N, Sumeet D. Folksonomy based adhoc community detection in online socialnetworks[EB/OL].[2012-08-22].http://link.springer.com/article/10.1007%2Fs1327801200819?LI=true#page1.
[45] Marek L, Borkur S, Alejandro J. Understanding and leveraging tag－basedrelations in online social networks[EB/OL].[2012-08-22]. http://dl.acm.org/ft_gateway.cfm?id=2310035&ftid=1259240&dwn=1.
[46] Symeonidis P, Nanopoulos A, Manolopoulos Y. Tag Recommendations Based on TensorDimensionality Reduction[EB/OL].[2012-08-22].http://www.bradblock. com/s3－website－us－west.amazonaws.com/Tag_Recommendations_Based_on_Tensor_Dimensionality_Reduction.pdf.
[47] Steffen rendle, Lars schmidit-thieme. Pairwise interaction tensor factorizeation for personalized tag recommendation[EB/OL].[2012-08-22].http://wume.cse.lehigh.edu/~ovd209/wsdm/proceedings/docs/p81.pdf.
[48] Ralf K, Peter F.Personalized topic-based tag recommendation [EB/OL].
[2013-02-28].http://www.sciencedirect.com/science/article/pii/S0925231211003985.
[49] CICHOCKIA, ZDUNEK R, PHAN A H, et al. Nonnegativematrix and tensorfactorizations: applications to exploratory multiway data analysis and blindsource separation[M].Hobolcen:Wiley,2009.
[50] KOLDA TG, BADER BW. Tensor decompositions and applications[J]. SIAM Review,2009,51(3):455－500.
[51] KROONENBERG PM. Applied multiway data analysis[M]. Hoboken: Wiley,2008.
[52] S.Rendle， L.BalbyMarinho， A.NanoPou los and L.Sehmidt－Thieme. LeamingOptimal ranking with tensor factorization for tag recommendation [C]. InProceedings of the15th ACM SIGKDD international conference on Knowledgediscovery and data mining ACM,2009:727－736.
[53] Deerwester S, Dumais S T, Furnas G W, et al. Indexing by latent semantic analysis
[EB/OL].[2012-08-18].http://www.cob.unt.edu/itds/faculty/evangelopoulos/dsci5910/LSA_Deerwester1990.pdf.
[54] Hofmann T. Probabilistic latent semantic indexing[EB/OL].[2012-08-18].http://lvk.cs.msu.su/~bruzz/articles/feature_Selection_clustering/Hofmann.SIGIR99.pdf.
[55] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems:A survey of the state of the art and possible extensions[J].IEEE transactionson knowledge and data eng-ineering,2005,17(6):734-749.
[56] Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation[EB/OL].[2012-08-22].http://admis.fudan.edu.cn/seminars/ppt/lecturelda.pdf.
[57] Umbrath A S, Wetzker R, Umbrath W, et al. A hybrid PLSA approach for warmercold start in folksonomy recommendation [EB/OL].[2012-08-23].http://www.dailabor.de/fileadmin/Files/Publikationen/Buchdatei/Said.pdf.
[58] Francisco E, José J A, Alberto C, Ontology of Folksonomy: A New ModelingMethod[EB/OL].[2013-02-28].http://www.gsd.unavarra.es/gsd/files/condep/EcAsCoVisaakm07f.pdf.
[59] T Gruber.Ontology of folksonomy: A mashup of applesand oranges[EB/OL].
[2013-02-28].http://www.igiglobal.com/article/ontology folksonomy mashapples oranges/2828.
[60] Godoy D, Amandi A． Hybrid Content and Tag-based Profiles for Recommendationin Collaborative Tagging Systems[EB/OL].2012-08-22].http://dblab.cs.nccu.edu.tw/presentation/980525/980525.pdf．
[61] Zhang Y, Feng B.Tag-based User Modeling Using Formal Concept Analysis[C].Proceedings of the8th IEEE International. Conference on Computer andInformation Technology. Sydney:IEEE，2008:485－490．
[62] Jschke R，Hotho A，Schmitz C，et al．Discovering Shared Conceptualizations inFolksonomies[J].Web Semantics: Science，Services and Agents on the World WideWeb,2008,6(1):38－53.
[63]曹高辉,毛进.基于协同标注的B2C电子商务个性化推荐系统研究[J].图书情报工作,2008,52(12):126－128.
[64]杨丹,曹俊.基于Web2.0的社会性标签推荐系统[J].重庆工学院学报:自然科学版.2008,(7):52－53.
[65]吴江.豆瓣中图书、标签、读者推荐构成的网络分布特征研究[J].计算机与网络，2009(3/4):145－148.
[66]邓双义.基于语义的标签推荐系统关键问题研究[D].上海:华东师范大学,2009.
[67]田莹颖.基于社会化标签系统的个性化信息推荐探讨[J].图书情报工作,2010,54(1):50－54.
[68] Konstas I, et al. Recommendation in the social Web[J].AIMagazine,2011,32(3):46－56.
[69] Kazienko P, Musial K. Recommendation framework for online social networks[C].4thInternational Atlantic web intelligence conference(AWIC2006),2006:111－120.
[70] Groh G, Ehmig C. Recommendations in taste related domains: Collaborativefiltering vs. social filtering[C].Proceedings of the2007International ACMConference on Supporting group Work,2007:127－136.
[71] Jung JJ, et al. Are you satisfied with your recommendation service?: Discoveringsocial networks for personalized mobile services[C].2ndKES InternationalSymposium on Agent and Multi－Agent Systems,2008;567－573.
[72] Hu J, et al. Personalized tag recommendation using social influence[J].Journalof Computer Science and Technology,2012,27(3):527－540.
[73] Wei D, et al. Effective mechanism for social recommendation of news[J].PhysicalStatistical Mechanics and Its Applications,2011,390(11):2117－2126.
[74] Walter F E, Battiston S, Schweitzer F. A model of a trust based recommendationsystem on a social network[J].Autonomous Agents and Multi Agent Systems,2008,16(1):57－74.
[75] Li Y M, Chen C W. A synthetical approach for blog recommendation Combining trust,social relation and semantic analysis[J],Expert Systems with Applications,2009,36(3):6536－6547.
[76]魏建良,朱庆华.基于社会化标注的个性化推荐研究进展[J].情报学报,2010(4):625－633.
[77]易明.社会化标签系统中基于组合策略的个性化知识推荐研究[J].情报科学,2011(7):1093－1097.
[78]冯敏.情报学中信息社会化推荐的理论研究[J].情报理论与实践,2011(8):25－30.
[79]任佩瑜，张莉，宋勇.基于复杂性科学的管理熵、管理耗散结构理论及其在企业组织与决策中的作用[J].管理世界,2001,(6):142－147
[80]曾国屏.超循环自组织理论[J].科学、技术与辩证法,1988(4):63－85.
[81]普利高津.非平衡系统的自组织[M].北京:科学出版社，1986:496－511
[82]哈肯.协同学[M].北京:科学出版社，1989
[83]沈小峰等.耗散结构论[M].上海:上海人民出版社,1987:90－102
[84] Barry Wellman. Network Analysis: Some Basic Principles[J].SociologicalTheory,1983,(1):157
[85][德]马克斯·韦伯.《社会科学方法论》[M].北京:华夏出版社，1999
[86] Barry Wellman. Structural Analysis：From Method and Metaphor to Theory andSubstance[M]. Cambridge: Cambridge University Press,1988:47
[87] Peter Marsden，Nan Lin. Social Structure and Network Analysis[M]. Beverly Hills:Sage Publications,1982:10
[88] Barry Wellman, S.D.Berkovitz. Social Structures: A Network Approach[M].Greenwich: JAI Press,1997:20
[89][美]曼纽尔·卡斯特.《网络社会的崛起》[M].北京:社会科学文献出版社,2003
[90] S.Wasserman, K.Faust. Social Network Analysis: Methods and Applications[M].Cambridge: Cambridge University Press,1994:20
[91] Michael Henry, Richard Kneller, Chris Milner. Trade, technology transfer andnational efficiency in developing countries. European Economic Review online，2010,23(11):337－342
[92] Forrester J.W. Industrial Dynamics: A Breakthrough for Decision Makers[J].Harvard Business Review,1958:36(4):37－66
[93] Forrester J.W. Industrial Dynamics[M]. Cambridge: Productivity Press,1961
[94]武慧娟,徐宝祥.基于知识图谱的社会化标签系统研究热点与前沿分析[J],情报科学,2012,30(10):1447－1451.
[95]张子柯.社会化标签系统的结构、演化和功能[J].上海理工大学学报,2011,33(5):445－451.
[96] Ding, Y., E.K. Jacob, Z. Zhang, etal. Perspectives on Social Tagging[J].Journalof the American Society for Information Science and Technology,2009,60(12):2388－2401.
[97]鲁晓明.Web2.0中社会性标签系统的复杂网络特性研究[J],现代情报,2007(12):64－66.
[98]李传殿,宣云干,鞠秀芳.社会化标注系统的复杂适应性分析[J].系统科学学报,2011,19(4):21－23.
[99]李静.社会化标注的语义聚类[J].情报科学,2011,29(7):1007－1009.
[100]陈丽霞,窦永香,秦春秀.利用社会化标签实现P2P语义推荐[J].图书情报工作2011,55(22):110－113.
[101] Fu WT,Kannampallil T, Kang R, etal. Semantic Imitation in Social Tagging [J].ACM Transactions on Computer Human Interaction.2010,17(3):1200－1237.
[102]沈小峰,吴彤,曾国屏.论系统的自组织演化[J].北京师范大学学报(社会科学版),1993(3):79－88.
[103]刘春茂,米国伟.基于Web2.0的网络用户群“社会性”行为的系统分析[J].图书情报工作,2010,54(20):32－35.
[104]万君,顾新.基于超循环理论的知识网络演化机理研究[J].情报科学,2012,28(6):1230－1232.
[105]刘刚.知识网络的超循环结构及协同演化[J].科技进步与对策,2007,24(8):145－148.
[106]王忠义,李纲.人际情报网络自组织机理研究[J].情报科学,2012,30(1):28－33.
[107]李鹏.Web2.0环境中用户生成内容的自组织[J].图书情报工作,2012,5(16):119－126．
[108]任福珍,卢桂兰.情报信息系统的耗散结构分析[J].情报杂志,2004(10):71－72．
[109] Tatsuhito Nagaya. Constraints programmed macro model of information selectionlimits of information processing and the basic principle of informationbehavior. Electronics and Communications in Japan,1996,79(9):1－14.
[110]李枫林,张银.环境下基于的用户信息行为过程建模研究[J].图书情报工作,2009,53(21):50－54.
[111]武慧娟.基于知识图谱的国内外个性化信息推荐比较研究[J],东北电力大学学报,2012,(7):30－34
[112]何黎,何跃,霍叶青.微博用户特征分析和核心用户挖掘[J].情报理论实践,2011,34(11):121－125.
[113]郭秋艳,何跃.新浪微博名人用户特征挖掘及效应研究[J].情报杂志,2011,34(11):112－116.
[114]杰里夫·里夫金,特德·霍华德.熵:一种新的世界观[M].上海译文出版社,1987.
[115]任佩瑜,张莉,宋勇.基于复杂性科学的管理熵、管理耗散结构理论及其在企业组织与决策中的作用[J].管理世界,2001(6):142－147.
[116]胡昌平,邵其赶,孙高岭.个性化信息服务中的用户偏好与行为分析[J].情报理论实践.2008,31(1):4－6.
[117] Maslow AH. Motivation and Personality[M].New York Addison Wesley Longman,1987:56－61.
[118]谢海涛,孟祥武.适应用户需求进化的个性化信息服务模型[J].电子学报.2011,39(3):643－648.
[119]胡昌平,胡吉明.网络服务环境下用户关系演化规律研究[J].中国图书馆学报,2011(3):004－010.
[120]让·梅松纳夫.《群体动力学》[M].殷世才,孙兆通,译.北京:商务印书馆,1997:1.
[121] RheinHold H. The virtual community: Homesteading on the electronic frontier
[M].New York Addison－Wesley,1993:334.
[122]王连喜,蒋盛益,庞观松.微薄用户关系挖掘研究综述[J].情报杂志,2012,31(12):91－97.
[123]张莉.社会网络视角下的用户关系与信息服务体系研究[J].情报科学,2013,31(7):41－45.
[124]胡吉明,张蔓蒂,黄如花.社会网络环境下用户关系对信息传播的影响作用[J].情报杂志,2013,32(6):181－185.
[125]曾国屏,李宏芳,张再兴.网络空间中主客体关系的演化规律及其对思想政治教育的启示[J].思想理论教育导刊,2006(1):37－41.
[126][英]布朗.群体过程[M].胡鑫,庆小飞,译.北京:中国轻工业出版社,2007:21.
[127] HranoveN Ner M S.The sNrenHNh of weakness[J].American Journal of SocioloHy,1973(78):1360－1380.
[128] Yin D, HonH L. SNrucNural Link Analysis and PredicNion in MicrobloHs[C].InProceedinHs of20NhACM Conference on InformaNion and KnowledHeManaHemenN(CIKM2011), HlasHow, ScoNland, UK,2011(10:24－28).
[129]王晓光.微博社区交流网络结构的实证分析[J].情报杂志,2011,30(2):199－203.
[130]王晓光.微博客用户行为特征与关系特征实证分析[J].图书情报工作,2010,54(14):66－70.
[131] Nan L.Social CapiNal:A Nheory of Social SNrucNure and AcNion[M].CambridHeUnuversiNy Press,2001:102.
[132]张鼐,周年喜.社会资本和个人动机对虚拟区知识共享影响的研究[J].情报理论实践.2012,35(7).
[133] MA M,AGARWAL R.Through a glass darkly: information technology design,identityverifycation，and knowledge contribution in online communities[J].Information Systems Research,2007,18(1):42－67.
[134] METZGER M J.Making sense of credibility on the Web:models ofr ebaluating onlineinformation and recommendations for future research[J].JASIST,2007,58(13):208－2091.
[135] AZZEDIN F,MAHESWARAN M.Evolving and managing trust in grid computingsystem[C]//IEEE Canadian Conference on Electrical&Computer Engineering,May,2002.
[136] DONDIO P,BARRETT S.Computational trust in Web content quality:a comparativeevaluation on the Wikipedia project [J].Informatica,2007(31):151－160.
[137] Wang Y,Vassilveva J.Trust and reputation model in peer－to－peernetworks[C].Proceedings of the3rdInternationa Conference on Peer－To－PeerComputing,2003:150－157.
[138]廖成林.虚拟营销中信任关系的影响因素与机制[J].管理世界,2004(6):149－150.
[139]张喜征.基于虚拟社区的网络信任管理模式研究[J].情报杂志,2006(4):20－22.
[140]辛自强,高芳芳,张梅.人际——群际信任的差异:测量与影响因素[J].上海师范大学学报,2013,42(1):76－79.
[141] Song F.Trust and reciprocity behavior and behavioral forecasts:Individualsversus group representatives[J].Games and Econonic Behavior,2008,62:675－696.
[142]张玥,朱庆华.学术博客交流网络的核心—边缘结构分析实证研究[J].图书情报工作.2009,53(53).
[143]刘军.整体网分析讲义[M].上海:世纪出版集团,2009:7.
[144]宗刚,赵晓东.基于K－核分析的中国啤酒品牌二分网络结构研究.北京工业大学学报.2013,39(6):936－940.
[145]任卓明,刘建国,邵凤等.复杂网络中最小K－核节点的传播能力分析.物理学报.2013,62(10):1－4.
[146]陈忠,盛毅华.现代系统科学学[C].上海:上海科学技术文献出版社,2005.05.
[147]苏越,谢新观,杨武金.思路逻辑创造方法[C].北京:中央广播电视大学出版社出版,1993(05).
[2]谭晨辉,刘青炎.Open API出现、起源与现状[J].程序员,2008(07):38－41.
[3]熊回香,陈珊,许颖颖.基于Web3.0的个性化信息聚合技术研究[J].情报理论与实践,2011.34(8):95－99.
[4]吴汉华,王子舟.从“Web3.0”到“图书馆3.0”[J].图书馆建设,2008(4):66－70.
[5]熊回香,王学东.面向Web3.0的分众分类研究[J].图书情报工作,2010,54(3):104－105.
[6]陈定权,武立斌.社会网络视角下的信息推荐[J].情报杂志,2007(11):37－42.
[7] Furner J. On Recommending[J].Journal of the American Society for InformationScience and Tecnology,2002,53(9):747－763
[8]胡吉明.社会化推荐服务研究述评[J].情报科学,2011,29(2):308－309.
[9]冯蕾,张宇光,唐丽.复杂网络理论在图书馆个性化推荐服务中的应用[J].情报理论与实践,2009,32(2):69－71.
[10] Chiu D KW, Leung H F, Lam K M. On the Making of Service Recommendations: AnAction Theory Based on Utility, Reputation, and Risk Attitude[J].ExpertSystems with Applications,2009,36(2):3293－3301.
[11]魏建良,朱庆华.社会化标注理论研究综述[J].中国图书馆学报,2009,35(184):88－96.
[12] Niwa S, Doi T, Honiden S. Web page recommender system based on folksonomy miningfor ITNG’06submissions[C].Information Technology: New Generations,2006.Third International Conference,2006:388－393.
[13] Sasaki A, Miyata T, Inazumi Y, et al. Web content recommendation system basedon similarities among contents cluster of social bookmark[J].In DB Web2006,2006:59－66.
[14] Michlmayr E, Cayzer S. Learning user profiles from tagging data and leveragingthem form personal(ized) information access[C].In Proceedings of the Workshopon Tagging and Metadata for Social Information Organization,16th InternationalWorld Wide Web Conference,Eigenverlag,2007.
[15] Walter F. E. A model of a trust based recommendation system on a social network[J].Autonomous Agents and Multi Agent Systems,2008,16(1):57－74.
[16] Li Y. M., et al. A synthetical approach for blog Recommendation: Combining trust,social relation, and semantic analysis[J].Expert Systems with Applications,2009,36(3):6536－6547.
[17] http://paper.sciencenet.cn/htmlpaper/201211813464616021710.shtm.2012118/20120301.
[18]任红娟,张志强.基于文献记录的科学知识图谱发展研究[J].情报杂志,2009(12):86－90.
[19] Golder SA, Bernardo A.. Usage patterns of collaborative tagging systems[J].Journal of information science,2006,32(2):198－208.
[20] HothoA, Jasehke R，Sehmi C，etal. Information retrieval in folksonomies: Searchand ranking[EB/OL].http://maps.cs.hut.fi/bib/Folksonomy Ontology/Information_Retrieval_in_Folksonomies__Search_and_Ranking.pdf，2012-04-25.
[21] Marlow, C., Naaman, M., Doyd, Detal. HT06, Tagging Paper, Taxonomy, Flickr,Academic Article, To Read[EB/OL]. http://www.stanford.edu/~koutrika/Readings/res/Default/p31marlow.pdf，2012-04-25.
[22] Mathes, A. Folksonomies Cooperative Classication and Communication ThroughShared Metadata[EB/OL].http://www.citeulike.org/group/862/article/90413，2012-04-25.
[23] Sen S, Shyong K., Al MR,et al.tagging,communities, vocabulary,evolution
[EB/OL].http://folk.uio.no/yennyo/readme/old/p181sen.pdf，2012-04-25.
[24] Ding, Y., E.K. Jacob, Z. Zhang, et al. Perspectives on Social Tagging[J].Journal of the American Society for Information Science and Technology,2009,60(12):2388－2401.
[25] Golder, S., B. A. Huberman,2006,Usage patterns of collaborative taggingsystems[J].Information System.2006,32(2):198－208.
[26] Kim HN, Rawashdeh M, Alghamdi A, et al. Folksonomy based personalized searchand ranking in social media services. Information Systems[J].2011,37(1):61－76.
[27] Kim HN, Roczniak A, Levy P, et al. Social media filtering based on collaborativetagging in semantic space[J].Multimedia Tools and Applications.2012,56(1):63－89.
[28] Millen D., Yang M., Whittaker S.,et al. Social bookmarking and exploratorysearch[EB/OL].http://research.microsoft.com/enus/um/people/ryenw/proceedings/ESI2007.pdf#page=9,2012-04-25.
[29] Belen BM A., Costa ME., Burguillo JC., et al. A hybrid content-based anditem-based collaborative filtering approach to recommend TV programs enhancedwith singular value decomposition[J].Information Sciences.2010,180(22):4290－4311.
[30] Adomavicius G, Tuzhilin A.Toward the next generation of recommender systems:A survey of the state of the art and possible extensions[J].IEEE transactionson knowledge and data engineering,2005,17(6):734－749.
[31] Balabanovic M. Content-Based, Collaborative, Recommendation.1997,40:66－80.
[32] Herlocker JL, Konstan JA, Terveen K, et al. Evaluating collaborative filteringrecommender systems.ACM transactions on information systems.2004,22(1):5－53.
[33] Upendra Shardanand, Pattie Maes. Social information filtering: algorithms forautomating“word of mouth”[J].Proceeding CHI'95Proceedings of the SIGCHIconference on Human factors in computing systems1995：210－217.
[34] Konstan J., Miller B., Maltz, et al.GroupLens: Applying Collaborative Filteringto Usenet News[J].Communications of the ACM,1997,40(3):77－87.
[35] Bonhard P., Sasse M.A. Knowing me, knowing you using profiles and socialnetworking to improve recommender systems[J].Bt Technology Journal,2006.24(3):84－98.
[36] Lambiotte R,Ａusloos M. Collaborative tagging as a tripartite network [EB/OL].
[2012-08-22].http://arxiv.org/pdf/cs/0512090.pdf.
[37] Zhang Zi-Ke, Zhou Tao, Zhang Yi-Cheng. Personalized recommendation viaintegrated diffusion on user-item-tag tripartite graphs[J].Physical,2010,389(1):179－186.
[38] Shang Ming-Sheng, ZhangZi-Ke, Zhou Tao. Collaborative filtering with diffusionbased similarity on tripartite graphs[J].Physical,2010,389(6):1259－1264.
[39] Cattuto C, Schmitz C, Baldassarri A, et al. Network properties of folksonomies[J].AI Communications,2007,20(4):245－262.
[40] Ghoshal G, Zlatic’V, Caldarelli G, et al. Random hypergraphs and theirapplications[EB/OL].[2012-08－22].http://arxiv.org/abs/09030419.
[41] Zlatic’v, Ghoshal G, Caldarelli G. Hypergraphs topological quantities fortagged social networks[EB/OL].[2012-08-22]. http://pre.aps.org/abstract/PRE/v80/i3/e036118.
[42] De Pessemier T, Deryckere T, Martens Luc. Context aware recommendations foruser generated content on a social network site[EB/OL].[2012-08-22].http://www.igiglobal.com/article/ontology folksonomy a shapeless oranges/2828.
[43] Kim HN, El Saddik Abdulmotaleb. Exploring social tagging for personalizedcommunity recommendations[J], USER MODELING AND USERADAPTED INTERACTION,2013,23(2-3):249－285.
[44] Vasanth N, Sumeet D. Folksonomy based adhoc community detection in online socialnetworks[EB/OL].[2012-08-22].http://link.springer.com/article/10.1007%2Fs1327801200819?LI=true#page1.
[45] Marek L, Borkur S, Alejandro J. Understanding and leveraging tag－basedrelations in online social networks[EB/OL].[2012-08-22]. http://dl.acm.org/ft_gateway.cfm?id=2310035&ftid=1259240&dwn=1.
[46] Symeonidis P, Nanopoulos A, Manolopoulos Y. Tag Recommendations Based on TensorDimensionality Reduction[EB/OL].[2012-08-22].http://www.bradblock. com/s3－website－us－west.amazonaws.com/Tag_Recommendations_Based_on_Tensor_Dimensionality_Reduction.pdf.
[47] Steffen rendle, Lars schmidit-thieme. Pairwise interaction tensor factorizeation for personalized tag recommendation[EB/OL].[2012-08-22].http://wume.cse.lehigh.edu/~ovd209/wsdm/proceedings/docs/p81.pdf.
[48] Ralf K, Peter F.Personalized topic-based tag recommendation [EB/OL].
[2013-02-28].http://www.sciencedirect.com/science/article/pii/S0925231211003985.
[49] CICHOCKIA, ZDUNEK R, PHAN A H, et al. Nonnegativematrix and tensorfactorizations: applications to exploratory multiway data analysis and blindsource separation[M].Hobolcen:Wiley,2009.
[50] KOLDA TG, BADER BW. Tensor decompositions and applications[J]. SIAM Review,2009,51(3):455－500.
[51] KROONENBERG PM. Applied multiway data analysis[M]. Hoboken: Wiley,2008.
[52] S.Rendle， L.BalbyMarinho， A.NanoPou los and L.Sehmidt－Thieme. LeamingOptimal ranking with tensor factorization for tag recommendation [C]. InProceedings of the15th ACM SIGKDD international conference on Knowledgediscovery and data mining ACM,2009:727－736.
[53] Deerwester S, Dumais S T, Furnas G W, et al. Indexing by latent semantic analysis
[EB/OL].[2012-08-18].http://www.cob.unt.edu/itds/faculty/evangelopoulos/dsci5910/LSA_Deerwester1990.pdf.
[54] Hofmann T. Probabilistic latent semantic indexing[EB/OL].[2012-08-18].http://lvk.cs.msu.su/~bruzz/articles/feature_Selection_clustering/Hofmann.SIGIR99.pdf.
[55] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems:A survey of the state of the art and possible extensions[J].IEEE transactionson knowledge and data eng-ineering,2005,17(6):734-749.
[56] Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation[EB/OL].[2012-08-22].http://admis.fudan.edu.cn/seminars/ppt/lecturelda.pdf.
[57] Umbrath A S, Wetzker R, Umbrath W, et al. A hybrid PLSA approach for warmercold start in folksonomy recommendation [EB/OL].[2012-08-23].http://www.dailabor.de/fileadmin/Files/Publikationen/Buchdatei/Said.pdf.
[58] Francisco E, José J A, Alberto C, Ontology of Folksonomy: A New ModelingMethod[EB/OL].[2013-02-28].http://www.gsd.unavarra.es/gsd/files/condep/EcAsCoVisaakm07f.pdf.
[59] T Gruber.Ontology of folksonomy: A mashup of applesand oranges[EB/OL].
[2013-02-28].http://www.igiglobal.com/article/ontology folksonomy mashapples oranges/2828.
[60] Godoy D, Amandi A． Hybrid Content and Tag-based Profiles for Recommendationin Collaborative Tagging Systems[EB/OL].2012-08-22].http://dblab.cs.nccu.edu.tw/presentation/980525/980525.pdf．
[61] Zhang Y, Feng B.Tag-based User Modeling Using Formal Concept Analysis[C].Proceedings of the8th IEEE International. Conference on Computer andInformation Technology. Sydney:IEEE，2008:485－490．
[62] Jschke R，Hotho A，Schmitz C，et al．Discovering Shared Conceptualizations inFolksonomies[J].Web Semantics: Science，Services and Agents on the World WideWeb,2008,6(1):38－53.
[63]曹高辉,毛进.基于协同标注的B2C电子商务个性化推荐系统研究[J].图书情报工作,2008,52(12):126－128.
[64]杨丹,曹俊.基于Web2.0的社会性标签推荐系统[J].重庆工学院学报:自然科学版.2008,(7):52－53.
[65]吴江.豆瓣中图书、标签、读者推荐构成的网络分布特征研究[J].计算机与网络，2009(3/4):145－148.
[66]邓双义.基于语义的标签推荐系统关键问题研究[D].上海:华东师范大学,2009.
[67]田莹颖.基于社会化标签系统的个性化信息推荐探讨[J].图书情报工作,2010,54(1):50－54.
[68] Konstas I, et al. Recommendation in the social Web[J].AIMagazine,2011,32(3):46－56.
[69] Kazienko P, Musial K. Recommendation framework for online social networks[C].4thInternational Atlantic web intelligence conference(AWIC2006),2006:111－120.
[70] Groh G, Ehmig C. Recommendations in taste related domains: Collaborativefiltering vs. social filtering[C].Proceedings of the2007International ACMConference on Supporting group Work,2007:127－136.
[71] Jung JJ, et al. Are you satisfied with your recommendation service?: Discoveringsocial networks for personalized mobile services[C].2ndKES InternationalSymposium on Agent and Multi－Agent Systems,2008;567－573.
[72] Hu J, et al. Personalized tag recommendation using social influence[J].Journalof Computer Science and Technology,2012,27(3):527－540.
[73] Wei D, et al. Effective mechanism for social recommendation of news[J].PhysicalStatistical Mechanics and Its Applications,2011,390(11):2117－2126.
[74] Walter F E, Battiston S, Schweitzer F. A model of a trust based recommendationsystem on a social network[J].Autonomous Agents and Multi Agent Systems,2008,16(1):57－74.
[75] Li Y M, Chen C W. A synthetical approach for blog recommendation Combining trust,social relation and semantic analysis[J],Expert Systems with Applications,2009,36(3):6536－6547.
[76]魏建良,朱庆华.基于社会化标注的个性化推荐研究进展[J].情报学报,2010(4):625－633.
[77]易明.社会化标签系统中基于组合策略的个性化知识推荐研究[J].情报科学,2011(7):1093－1097.
[78]冯敏.情报学中信息社会化推荐的理论研究[J].情报理论与实践,2011(8):25－30.
[79]任佩瑜，张莉，宋勇.基于复杂性科学的管理熵、管理耗散结构理论及其在企业组织与决策中的作用[J].管理世界,2001,(6):142－147
[80]曾国屏.超循环自组织理论[J].科学、技术与辩证法,1988(4):63－85.
[81]普利高津.非平衡系统的自组织[M].北京:科学出版社，1986:496－511
[82]哈肯.协同学[M].北京:科学出版社，1989
[83]沈小峰等.耗散结构论[M].上海:上海人民出版社,1987:90－102
[84] Barry Wellman. Network Analysis: Some Basic Principles[J].SociologicalTheory,1983,(1):157
[85][德]马克斯·韦伯.《社会科学方法论》[M].北京:华夏出版社，1999
[86] Barry Wellman. Structural Analysis：From Method and Metaphor to Theory andSubstance[M]. Cambridge: Cambridge University Press,1988:47
[87] Peter Marsden，Nan Lin. Social Structure and Network Analysis[M]. Beverly Hills:Sage Publications,1982:10
[88] Barry Wellman, S.D.Berkovitz. Social Structures: A Network Approach[M].Greenwich: JAI Press,1997:20
[89][美]曼纽尔·卡斯特.《网络社会的崛起》[M].北京:社会科学文献出版社,2003
[90] S.Wasserman, K.Faust. Social Network Analysis: Methods and Applications[M].Cambridge: Cambridge University Press,1994:20
[91] Michael Henry, Richard Kneller, Chris Milner. Trade, technology transfer andnational efficiency in developing countries. European Economic Review online，2010,23(11):337－342
[92] Forrester J.W. Industrial Dynamics: A Breakthrough for Decision Makers[J].Harvard Business Review,1958:36(4):37－66
[93] Forrester J.W. Industrial Dynamics[M]. Cambridge: Productivity Press,1961
[94]武慧娟,徐宝祥.基于知识图谱的社会化标签系统研究热点与前沿分析[J],情报科学,2012,30(10):1447－1451.
[95]张子柯.社会化标签系统的结构、演化和功能[J].上海理工大学学报,2011,33(5):445－451.
[96] Ding, Y., E.K. Jacob, Z. Zhang, etal. Perspectives on Social Tagging[J].Journalof the American Society for Information Science and Technology,2009,60(12):2388－2401.
[97]鲁晓明.Web2.0中社会性标签系统的复杂网络特性研究[J],现代情报,2007(12):64－66.
[98]李传殿,宣云干,鞠秀芳.社会化标注系统的复杂适应性分析[J].系统科学学报,2011,19(4):21－23.
[99]李静.社会化标注的语义聚类[J].情报科学,2011,29(7):1007－1009.
[100]陈丽霞,窦永香,秦春秀.利用社会化标签实现P2P语义推荐[J].图书情报工作2011,55(22):110－113.
[101] Fu WT,Kannampallil T, Kang R, etal. Semantic Imitation in Social Tagging [J].ACM Transactions on Computer Human Interaction.2010,17(3):1200－1237.
[102]沈小峰,吴彤,曾国屏.论系统的自组织演化[J].北京师范大学学报(社会科学版),1993(3):79－88.
[103]刘春茂,米国伟.基于Web2.0的网络用户群“社会性”行为的系统分析[J].图书情报工作,2010,54(20):32－35.
[104]万君,顾新.基于超循环理论的知识网络演化机理研究[J].情报科学,2012,28(6):1230－1232.
[105]刘刚.知识网络的超循环结构及协同演化[J].科技进步与对策,2007,24(8):145－148.
[106]王忠义,李纲.人际情报网络自组织机理研究[J].情报科学,2012,30(1):28－33.
[107]李鹏.Web2.0环境中用户生成内容的自组织[J].图书情报工作,2012,5(16):119－126．
[108]任福珍,卢桂兰.情报信息系统的耗散结构分析[J].情报杂志,2004(10):71－72．
[109] Tatsuhito Nagaya. Constraints programmed macro model of information selectionlimits of information processing and the basic principle of informationbehavior. Electronics and Communications in Japan,1996,79(9):1－14.
[110]李枫林,张银.环境下基于的用户信息行为过程建模研究[J].图书情报工作,2009,53(21):50－54.
[111]武慧娟.基于知识图谱的国内外个性化信息推荐比较研究[J],东北电力大学学报,2012,(7):30－34
[112]何黎,何跃,霍叶青.微博用户特征分析和核心用户挖掘[J].情报理论实践,2011,34(11):121－125.
[113]郭秋艳,何跃.新浪微博名人用户特征挖掘及效应研究[J].情报杂志,2011,34(11):112－116.
[114]杰里夫·里夫金,特德·霍华德.熵:一种新的世界观[M].上海译文出版社,1987.
[115]任佩瑜,张莉,宋勇.基于复杂性科学的管理熵、管理耗散结构理论及其在企业组织与决策中的作用[J].管理世界,2001(6):142－147.
[116]胡昌平,邵其赶,孙高岭.个性化信息服务中的用户偏好与行为分析[J].情报理论实践.2008,31(1):4－6.
[117] Maslow AH. Motivation and Personality[M].New York Addison Wesley Longman,1987:56－61.
[118]谢海涛,孟祥武.适应用户需求进化的个性化信息服务模型[J].电子学报.2011,39(3):643－648.
[119]胡昌平,胡吉明.网络服务环境下用户关系演化规律研究[J].中国图书馆学报,2011(3):004－010.
[120]让·梅松纳夫.《群体动力学》[M].殷世才,孙兆通,译.北京:商务印书馆,1997:1.
[121] RheinHold H. The virtual community: Homesteading on the electronic frontier
[M].New York Addison－Wesley,1993:334.
[122]王连喜,蒋盛益,庞观松.微薄用户关系挖掘研究综述[J].情报杂志,2012,31(12):91－97.
[123]张莉.社会网络视角下的用户关系与信息服务体系研究[J].情报科学,2013,31(7):41－45.
[124]胡吉明,张蔓蒂,黄如花.社会网络环境下用户关系对信息传播的影响作用[J].情报杂志,2013,32(6):181－185.
[125]曾国屏,李宏芳,张再兴.网络空间中主客体关系的演化规律及其对思想政治教育的启示[J].思想理论教育导刊,2006(1):37－41.
[126][英]布朗.群体过程[M].胡鑫,庆小飞,译.北京:中国轻工业出版社,2007:21.
[127] HranoveN Ner M S.The sNrenHNh of weakness[J].American Journal of SocioloHy,1973(78):1360－1380.
[128] Yin D, HonH L. SNrucNural Link Analysis and PredicNion in MicrobloHs[C].InProceedinHs of20NhACM Conference on InformaNion and KnowledHeManaHemenN(CIKM2011), HlasHow, ScoNland, UK,2011(10:24－28).
[129]王晓光.微博社区交流网络结构的实证分析[J].情报杂志,2011,30(2):199－203.
[130]王晓光.微博客用户行为特征与关系特征实证分析[J].图书情报工作,2010,54(14):66－70.
[131] Nan L.Social CapiNal:A Nheory of Social SNrucNure and AcNion[M].CambridHeUnuversiNy Press,2001:102.
[132]张鼐,周年喜.社会资本和个人动机对虚拟区知识共享影响的研究[J].情报理论实践.2012,35(7).
[133] MA M,AGARWAL R.Through a glass darkly: information technology design,identityverifycation，and knowledge contribution in online communities[J].Information Systems Research,2007,18(1):42－67.
[134] METZGER M J.Making sense of credibility on the Web:models ofr ebaluating onlineinformation and recommendations for future research[J].JASIST,2007,58(13):208－2091.
[135] AZZEDIN F,MAHESWARAN M.Evolving and managing trust in grid computingsystem[C]//IEEE Canadian Conference on Electrical&Computer Engineering,May,2002.
[136] DONDIO P,BARRETT S.Computational trust in Web content quality:a comparativeevaluation on the Wikipedia project [J].Informatica,2007(31):151－160.
[137] Wang Y,Vassilveva J.Trust and reputation model in peer－to－peernetworks[C].Proceedings of the3rdInternationa Conference on Peer－To－PeerComputing,2003:150－157.
[138]廖成林.虚拟营销中信任关系的影响因素与机制[J].管理世界,2004(6):149－150.
[139]张喜征.基于虚拟社区的网络信任管理模式研究[J].情报杂志,2006(4):20－22.
[140]辛自强,高芳芳,张梅.人际——群际信任的差异:测量与影响因素[J].上海师范大学学报,2013,42(1):76－79.
[141] Song F.Trust and reciprocity behavior and behavioral forecasts:Individualsversus group representatives[J].Games and Econonic Behavior,2008,62:675－696.
[142]张玥,朱庆华.学术博客交流网络的核心—边缘结构分析实证研究[J].图书情报工作.2009,53(53).
[143]刘军.整体网分析讲义[M].上海:世纪出版集团,2009:7.
[144]宗刚,赵晓东.基于K－核分析的中国啤酒品牌二分网络结构研究.北京工业大学学报.2013,39(6):936－940.
[145]任卓明,刘建国,邵凤等.复杂网络中最小K－核节点的传播能力分析.物理学报.2013,62(10):1－4.
[146]陈忠,盛毅华.现代系统科学学[C].上海:上海科学技术文献出版社,2005.05.
[147]苏越,谢新观,杨武金.思路逻辑创造方法[C].北京:中央广播电视大学出版社出版,1993(05).