NDLI: Query-sets: using implicit feedback and query patterns to organize web documents

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Personalized web exploration with task models

Query-sets: using implicit feedback and query patterns to organize web documents

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Query-sets: using implicit feedback and query patterns to organize web documents

Content Provider	ACM Digital Library
Author	Baeza-Yates, Ricardo Poblete, Barbara
Abstract	In this paper we present a new document representation model based on implicit user feedback obtained from search engine queries. The main objective of this model is to achieve better results in non-supervised tasks, such as clustering and labeling, through the incorporation of usage data obtained from search engine queries. This type of model allows us to discover the motivations of users when visiting a certain document. The terms used in queries can provide a better choice of features, from the user's point of view, for summarizing the Web pages that were clicked from these queries. In this work we extend and formalize as "query model" an existing but not very well known idea of "query view" for document representation. Furthermore, we create a novel model based on "frequent query patterns" called the "query-set model". Our evaluation shows that both "query-based" models outperform the vector-space model when used for clustering and labeling documents in a website. In our experiments, the query-set model reduces by more than 90% the number of features needed to represent a set of documents and improves by over 90% the quality of the results. We believe that this can be explained because our model chooses better features and provides more accurate labels according to the user's expectations.
Starting Page	41
Ending Page	50
Page Count	10
File Format	PDF
ISBN	9781605580852
DOI	10.1145/1367497.1367504
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-04-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Usage mining Feature selection Web page organization Search engine queries Labeling
Content Type	Text
Resource Type	Article

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