Research On Topic Clustering Model Of Socail Tagging Based On Bayesian Theory

With the development and improvement of Web 2.0 technology, social tagging emerged. Social tagging proposed by adhering to the characteristics of freedom and initiative about users'behaviors. Marked in the social environment, users set their own understanding of the relevant information resources to add the right tags, and users can refer other people to mark the label used. Mechanism to achieve this mark, making information users according to their demand for resources to select them, and according to their knowledge of resources to them, to embody the initiative of social tagging systems and personal characteristics.However, due to social tagging itself is a bottom-up label, which prompted this "right" tag, and there is no uniform rules to be binding, you can use a few phrases to describe the specific resources obviously, but because of the user's knowledge and understanding of differences in background, often marked on the information resources generated when the label ambiguity, synonymy, polysemy and so on with the form. At the same time ,in the past rarely had marked the current view of network resources is often ignored by users of information, this will cause a lot of great value to the network resources are ignored, these phenomena will give new users access to search and bring access to information resources great distress.For these questions, this paper Bayesian clustering algorithm combined with the topic of social tagging environment the theme of information resources effectively mining large amounts of user annotation results for a particular resource sets generated some label Clear and specific resources are classified eventually come to contain only a small number of representative labels set. The main contribution of this paper has the following aspects:(1) Marked by the presence of the community of polysemy, synonyms, and so the theory of the text mining mining theory applied to the latent semantic social tagging up. It solve user's semantic confusing effectivly in the process of annotation by building resources– tag matrix to mining t semantic space between them ;(2) Use of three Bayesian network and build a topic based on latent Dirichlet allocation, and on this basis, the subject of mining and its potential to effectively subtotals;(3) Bayesian theory with the prior knowledge and sample space, and put forward the topic of space classification, identification of resources for further refinement of the property, so that the first two aspects have been further improved.Above research not only enriched the information organization and retrieval relevant theory, but also for information theme and user preferences recognition provides an effective way.