建立到数据存储的连接、查找标注为实体的所有类、配置持久化逻辑以将这些类绑定到数据存储中的实体，整个过程不可能快速完成。
Setting up connectivity to a data store, finding all classes annotated as entities, and configuring the persistence logic to bind these classes to entities in the data store is not a quick operation.
首先是明确了标签对于用户模型的意义，接着，从用户、资源和标签三个角度对基于社会化标注的聚类算法进行了讨论。
Firstly, tag 's meaning for user profile is proved, and clustering algorithms based on social tagging from the aspects of users, resources and tags are discussed.
最后，分析了社会化标注中个性化信息推荐的研究，发现借助矩阵、聚类和网络的分析是三种主要思路。
Finally, studies on personalized information recommendation based on social tagging are analyzed, and find matrix, clustering and network analysis are three primarily methods.
为了改进当前社会化标注系统在标签浏览和检索方面的弱点，提出一种基于加权网络分割的社会性标签聚类算法。
This paper proposes a clustering algorithm of social tags based on weighed network division for the purpose of improving browsing and retrieval in existing social annotation system.
为了改进当前社会化标注系统在标签浏览和检索方面的弱点，提出一种基于加权网络分割的社会性标签聚类算法。
This paper proposes a clustering algorithm of social tags based on weighed network division for the purpose of improving browsing and retrieval in existing social annotation system.
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