NDLI: Clustering Search Engine Suggests by Modeling Topics of Web Pages collected with Suggests

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Clustering Search Engine Suggests by Modeling Topics of Web Pages collected with Suggests

Content Provider	ACM Digital Library
Author	Chen, Lei Utsuro, Takehito Kawada, Yasuhide Doi, Syunya Imada, Takakazu Inoue, Yusuke
Abstract	In this paper, we address the issue of how to overview the knowledge of a given query keyword. We especially focus on concerns of those who search for Web pages with a given query keyword, and study how to efficiently overview the whole list of Web search information needs of a given query keyword. First, we collect Web search information needs of a given query keyword through search engine suggests. Although we collect up to around 1,000 suggests given a query keyword, some of them are redundant in that they originate from almost the same Web search information needs. In order to aggregate such redundant search engine suggests, we take an approach of clustering search engine suggests based on a topic model. Evaluation result shows that the proposed clustering approach proves to be quite useful for efficiently overviewing Web search information needs of a given query keyword. We also develop an interface system for overviewing those aggregated search engine suggests of a given query keyword as well as links to top ranked Web pages that are closely related to those aggregated search engine suggests.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450341424
DOI	10.1145/2857546.2857597
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-01-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Search engine suggest Web search information need Overview Clustering Topic model
Content Type	Text
Resource Type	Article

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