摘要： 针对现有查询扩展缺陷，提出基于用户查询行为和词间完全加权关联规则挖掘的相关反馈查询扩展算法。在不改变用户查询信息习惯的前提下，无须用户参与，根据用户查询行为判断初检文档的相关性，提取相关的初检文档，挖掘与原查询相关的关联规则，构造规则库，从中提取与原查询相关的扩展词，实现查询扩展。实验结果表明，该算法能提高信息检索性能，具有很好的应用前景。

关键词: 查询扩展, 关联规则, 相关反馈, 信息检索

Abstract: Aiming at the limitations of existing query expansion, this paper proposes a novel query expansion algorithm of relevance feedback based on users’ query behaviors, as well as the technique of item-all-weighted association rule mining in retrieved relevance documents. According to the duration of user’s clicking and browsing, or the existence of some querying behaviors such as downloading, this algorithm is able to determine whether a document is related to users’ query intentions and interests, automatically extract those item-all-weighted association rules related to original query from retrieved relevance documents to construct an association rules-based database, and collect terms related original query as expansion terms from the database. Experimental results show the retrieval performance of the algorithm is improved remarkably.

Key words: query expansion, association rules, relevance feedback, information retrieval

中图分类号: 

• TP391