Learning to detect task boundaries of query session

ISCAS OpenIR

	Learning to detect task boundaries of query session
	Zhang, Zhenzhong (1); Sun, Le (1); Han, Xianpei (1)
	2013
会议名称	22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
页码	1885-1888
会议日期	October 27, 2013 - November 1, 2013
会议地点	San Francisco, CA, United states
收录类别	EI
出版地	Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States
ISBN	9781450322638
部门归属	(1) Institute of Software, Chinese Academy of Sciences, Beijing, China
摘要	To accomplish a search task and satisfy a single information need, users usually submit a series of queries to web search engines. It is useful for web search engines to detect the task boundaries in a series of successive queries. Traditional task boundary detection methods are based on time gap and lexical comparisons, which often suffer from the vocabulary gap problem, that is, the topically related queries may not share any common words. In this paper we learn hidden topics from query log and leverage them to resolve the vocabulary gap problem. Unlike other external knowledge resources, such as WordNet and Wikipedia, the hidden topics discovered from query log cover long tail queries, which is useful to detect task boundaries. Experimental results on dataset from real world query log demonstrate that the proposed method achieves significant quality enhancement. Copyright © 2013 ACM.; To accomplish a search task and satisfy a single information need, users usually submit a series of queries to web search engines. It is useful for web search engines to detect the task boundaries in a series of successive queries. Traditional task boundary detection methods are based on time gap and lexical comparisons, which often suffer from the vocabulary gap problem, that is, the topically related queries may not share any common words. In this paper we learn hidden topics from query log and leverage them to resolve the vocabulary gap problem. Unlike other external knowledge resources, such as WordNet and Wikipedia, the hidden topics discovered from query log cover long tail queries, which is useful to detect task boundaries. Experimental results on dataset from real world query log demonstrate that the proposed method achieves significant quality enhancement. Copyright © 2013 ACM.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16649
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Zhang, Zhenzhong ,Sun, Le ,Han, Xianpei . Learning to detect task boundaries of query session[C]. Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States,2013:1885-1888.