Recommending high-utility search engine queries via a query-recommending model

doi:10.1016/j.neucom.2015.04.076

	Recommending high-utility search engine queries via a query-recommending model
	Wang, JianGuo 1; Huang, Joshua Zhexue 2; Guo, Jiafeng 3; Lan, Yanyan 3
	2015-11-01
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	167 页码:195-208
摘要	Query recommendation technology is of great importance for search engines, because it can assist users to find the information they require. Many query recommendation algorithms have been proposed, but they all aim to recommend similar queries and cannot guarantee the usefulness of the recommended queries. In this paper, we argue that it is more important to recommend high-utility queries, i.e., queries that would induce users to search for more useful information. For this purpose, we propose a query-recommending model to rank candidate queries according to their utilities and to recommend those that are useful to users. The query-recommending model ranks a candidate query by assessing the joint probability that the query is selected by the user, that the obtained search results are subsequently clicked by the user, and that the clicked search results ultimately satisfy the user's information need. Three utilities were defined to solve the model: query-level utility, representing the attractiveness of a query to the user; perceived utility, measuring the user's probability of clicking on the search results; and posterior utility, measuring the useful information obtained by the user from the clicked search results. The methods that were used to compute these three utilities from the query log data are presented. The experimental results that were obtained by using real query log data demonstrated that the proposed query-recommending model outperformed six other baseline methods in generating more useful recommendations. (C) 2015 Elsevier B.V. All rights reserved.
关键词	Query recommendation Query log analysis Query ranking Recommendation methods
DOI	10.1016/j.neucom.2015.04.076
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61473194]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000358808500022
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：19[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/2XEOYT63/9500
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, JianGuo
作者单位	1.Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen Key Lab High Performance Data Min, Shenzhen Inst Adv Technol,Chinese Acad Sci, Shenzhen 518055, Peoples R China 2.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, JianGuo,Huang, Joshua Zhexue,Guo, Jiafeng,et al. Recommending high-utility search engine queries via a query-recommending model[J]. NEUROCOMPUTING,2015,167:195-208.
APA	Wang, JianGuo,Huang, Joshua Zhexue,Guo, Jiafeng,&Lan, Yanyan.(2015).Recommending high-utility search engine queries via a query-recommending model.NEUROCOMPUTING,167,195-208.
MLA	Wang, JianGuo,et al."Recommending high-utility search engine queries via a query-recommending model".NEUROCOMPUTING 167(2015):195-208.