Modeling users' search sessions for high utility query recommendation

doi:10.1007/s10791-016-9287-1

	Modeling users' search sessions for high utility query recommendation
	Guo, Jiafeng 1; Zhu, Xiaofei 2; Lan, Yanyan 1; Cheng, Xueqi 1
	2017-02-01
发表期刊	INFORMATION RETRIEVAL JOURNAL
ISSN	1386-4564
卷号	20 期号:1 页码:4-24
摘要	Query recommendation has long been considered a key feature of search engines, which can improve users' search experience by providing useful query suggestions for their search tasks. Most existing approaches on query recommendation aim to recommend relevant queries, i.e., alternative queries similar to a user's initial query. However, the ultimate goal of query recommendation is to assist users to reformulate queries so that they can accomplish their search task successfully and quickly. Only considering relevance in query recommendation is apparently not directly toward this goal. In this paper, we argue that it is more important to directly recommend queries with high utility, i.e., queries that can better satisfy users' information needs. For this purpose, we attempt to infer query utility from users' sequential search behaviors recorded in their search sessions. Specifically, we propose a dynamic Bayesian network, referred as Query Utility Model (QUM), to capture query utility by simultaneously modeling users' reformulation and click behaviors. We then recommend queries with high utility to help users better accomplish their search tasks. We empirically evaluated the performance of our approach on a publicly released query log by comparing with the state-of-the-art methods. The experimental results show that, by recommending high utility queries, our approach is far more effective in helping users find relevant search results and thus satisfying their information needs.
关键词	Search behavior Query utility Dynamic Bayesian network Query recommendation
DOI	10.1007/s10791-016-9287-1
收录类别	SCI
语种	英语
资助项目	National Basic Research Program of China (973 Program) of China[2014CB340401] ; National Key Research and Development Program of China[2016YFB1000902] ; National Natural Science Foundation of China[61472401] ; National Natural Science Foundation of China[61425016] ; National Natural Science Foundation of China[61203298]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000394184100002
出版者	SPRINGER
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7527
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Guo, Jiafeng
作者单位	1.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing, Peoples R China 2.Chongqing Univ Technol, 69 Hongguang Rd, Chongqing, Peoples R China
推荐引用方式 GB/T 7714	Guo, Jiafeng,Zhu, Xiaofei,Lan, Yanyan,et al. Modeling users' search sessions for high utility query recommendation[J]. INFORMATION RETRIEVAL JOURNAL,2017,20(1):4-24.
APA	Guo, Jiafeng,Zhu, Xiaofei,Lan, Yanyan,&Cheng, Xueqi.(2017).Modeling users' search sessions for high utility query recommendation.INFORMATION RETRIEVAL JOURNAL,20(1),4-24.
MLA	Guo, Jiafeng,et al."Modeling users' search sessions for high utility query recommendation".INFORMATION RETRIEVAL JOURNAL 20.1(2017):4-24.