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	Actively predicting diverse search intent from user browsing behaviors
	Cheng, Zhicong ; Gao, Bin ; Liu, Tie-Yan
	2010
英文摘要	This paper is concerned with actively predicting search intent from user browsing behavior data. In recent years, great attention has been paid to predicting user search intent. However, the prediction was mostly passive because it was performed only after users submitted their queries to search engines. It is not considered why users issued these queries, and what triggered their information needs. According to our study, many information needs of users were actually triggered by what they have browsed. That is, after reading a page, if a user found something interesting or unclear, he/she might have the intent to obtain further information and accordingly formulate a search query. Actively predicting such search intent can benefit both search engines and their users. In this paper, we propose a series of technologies to fulfill this task. First, we extract all the queries that users issued after reading a given page from user browsing behavior data. Second, we learn a model to effectively rank these queries according to their likelihoods of being triggered by the page. Third, since search intents can be quite diverse even if triggered by the same page, we propose an optimization algorithm to diversify the ranked list of queries obtained in the second step, and then suggest the list to users. We have tested our approach on large-scale user browsing behavior data obtained from a commercial search engine. The experimental results have shown that our approach can predict meaningful queries for a given page, and the search performance for these queries can be significantly improved by using the triggering page as contextual information. ? 2010 International World Wide Web Conference Committee (IW3C2).; EI; 0
语种	英语
DOI标识	10.1145/1772690.1772714
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/325840]
专题	软件与微电子学院
推荐引用方式 GB/T 7714	Cheng, Zhicong,Gao, Bin,Liu, Tie-Yan. Actively predicting diverse search intent from user browsing behaviors. 2010-01-01.

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