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Coping with noisy search experiences

Author(s)
Champin, Pierre-Antoine  
Briggs, Peter  
Coyle, Maurice  
Smyth, Barry  
Uri
http://hdl.handle.net/10197/1999
Date Issued
2010-05
Date Available
2010-05-20T15:37:03Z
Abstract
The so-called Social Web has helped to change the very nature of the Internet by emphasising the role of our online experiences as new forms of content and service knowledge. In this paper we describe an approach to improving main-stream Web search by harnessing the search experiences of groups of like-minded searchers. We focus on the HeyStaks system (www.heystaks.com) and look in particular at the experiential knowledge that drives its search recommendations. Specifically we describe how this knowledge can be noisy, and we describe and evaluate a recommendation technique for coping with this noise and discuss how it may be incorporated into HeyStaks as a useful feature.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
Elsevier
Journal
Knowledge-Based Systems
Volume
23
Issue
4
Start Page
287
End Page
294
Copyright (Published Version)
2009 Elsevier B.V.
Subjects

Experience

Web search

Recommender system

Subject – LCSH
Internet searching
Recommender systems (Information filtering)
DOI
10.1016/j.knosys.2009.11.011
Web versions
http://dx.doi.org/10.1016/j.knosys.2009.11.011
Language
English
Status of Item
Peer reviewed
ISSN
0950-7051
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-sa/1.0/
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jkbs09.pdf

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489.08 KB

Format

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Checksum (MD5)

10cb16b5d66e1cb540edaaf6f8e272ec

Owning collection
Computer Science Research Collection
Mapped collections
CLARITY Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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