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Modeling users' search sessions for high utility query recommendation

文献类型：期刊论文

作者Guo, Jiafeng1; Zhu, Xiaofei2; Lan, Yanyan1; Cheng, Xueqi1
刊名INFORMATION RETRIEVAL JOURNAL
出版日期2017-02-01
卷号20期号:1页码:4-24
关键词Search behavior Query utility Dynamic Bayesian network Query recommendation
ISSN号1386-4564
DOI10.1007/s10791-016-9287-1
英文摘要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.
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
语种英语
WOS记录号WOS:000394184100002
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/7526]  
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.

入库方式： OAI收割

来源：计算技术研究所

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