基于智能聚类的相关度内容检索方法

为了提高内容检索的相关度与检索效率,基于信息系统理论与自组织神经网络理论,提出基于智能聚类的相关度检索方法,并设计了检索算法.经过训练的自组织神经网络通过对查询需求进行聚类,使得内容的检索只在与查询需求同类的文本内容中进行,提高了检索效率,并通过在同一个向量空间对查询向量与文本内容的语义向量进行相似度衡量,为用户选择更相关的内容提供依据.设计开发了基于智能聚类的内容检索试验平台,验证了该方法的有效性.

Method of Intelligent Clustering Based Correlativity Content Retrieval

The paper emphasizes on the correlativity content retrieving method that are studied to compare the incoming query demand of the user with the content provided by the content management system.Similarity measures are done in the content vector space and the correlativity are ranked during the process of the content retrieval.The process and the algorithm of the correlativity content retrieving methods are proposed and the validity of the algorithm is analyzed.The trained self-organization neural network is used to cluster the query demand and the matching work is just done in the classification the query belongs to.The policy of intelligent clustering based correlativity content retrieval can suggest the different users how correlative the content is to their query demands so that the users can quickly select the content they concerns.