%0 Journal Article
%T A Statistical Query Expansion Model Based on Query Logs<br>基于用户日志的查询扩展统计模型
%A CUI Hang
%A WEN Ji-Rong
%A LI Min-Qiang
%A <br>崔航
%A 文继荣
%A 李敏强
%J 软件学报
%D 2003
%I 
%X Ambiguity of query terms has been a long-standing problem in information retrieval field, which becomes more serious in Web searching. A method for automatic query expansion based on query logs obtained from users?daily usage is suggested. This model establishes probabilistic relationship between terms in documents and in user queries through statistical learning from the log, and selects high-related expansion terms based on Bayesian theory. These expansion terms are added into the original query to formulate a new one in order to improve the effectiveness of retrieval. Experimental results show that this technique is more adaptive to Web searching, and can improve the precision of document retrieval markedly compared with conventional ones.
%K information retrieval
%K query expansion
%K user log
%K log mining<br>信息检索
%K 查询扩展
%K 用户日志
%K 日志挖掘
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=DA4ED681165C3AC4&yid=D43C4A19B2EE3C0A&vid=F3583C8E78166B9E&iid=9CF7A0430CBB2DFD&sid=087664F66E31A190&eid=7C7B8F426F4B99BA&journal_id=1000-9825&journal_name=软件学报&referenced_num=20&reference_num=10