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	QRM: A Probabilistic Model for Search Engine Query Recommendation
	Wang, JianGuo; Huang, Joshua Zhexue
	2014
会议名称	International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014
会议地点	Tainan, TAIWAN
英文摘要	This paper proposes a query ranking model (QRM) for query recommendation to the Web users of a search engine. Given an initial query in a search session,a set of queries for the user to select as the next query are ranked based on the joint probability that the query is to be selected by the user and that the result of the query is to be clicked by the user, and that the clicked result will satisfy the user's information requirement. We define three utilities to solve the model, including a query level utility and two document level utilities that are the perceived utility representing user's action on the query result and the posterior utility representing user's satisfaction on the search result. We present the methods to compute the three utilities from the query log data. Experiment results on realquery log data have demonstrated that the proposed query ranking model outperformed six baseline methods in generating recommendation queries.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/6036]
专题	深圳先进技术研究院_数字所
作者单位	2014
推荐引用方式 GB/T 7714	Wang, JianGuo,Huang, Joshua Zhexue. QRM: A Probabilistic Model for Search Engine Query Recommendation[C]. 见:International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014. Tainan, TAIWAN.

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