Research On Personalized Information Recommendation Model In The Social Tagging System

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.