Query Expansion of Relevance Feedback Based on Users' Query Behaviors and Association Rules
Yan Xiaowei · Jisuanji gongcheng · 2009
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.