基于兴趣模型的查询扩展

针对信息检索中查询与文档集之间可能存在的“词不匹配”问题，基于兴趣模型提出一种将概念化的兴趣知识与向量空间模型相结合的查询扩展方法。该方法能根据阈值来判断查询扩展是否可行。用户的兴趣偏好是通过Agent代理实时获取的，兴趣知识采用HNC（Hierarchical Network of Concepts, 概念层次网络）理论的概念符号体系表达，这样便于计算概念之间的相似度。实验结果表明，经过查询扩展后的结果相对于未加入查询扩展的结果在性能上提高了29.1%。

Query Expansion Based on Interest Model

To solve the problem that words mismatch between the retrieval and the documents in the query information , this paper proposes a query expansion method based on interest model , and it is combined with conceptual vector space model , and can judge whether it is feasible to query expansion according to the threshold value .In this method, user’ s interest is mined by Agent in real time, and the intellectual interests are expressed by the HNC （hierarchical network of concepts ）.As a result, it is very convenient to calculate the similarity between concepts .Experimental result shows that the performance has 29.1%improvement by using the query expansion compared to the non-query expansion .