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  5. Collaboration, Reputation and Recommender Systems in Social Web Search
 
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Collaboration, Reputation and Recommender Systems in Social Web Search

Author(s)
Smyth, Barry  
Coyle, Maurice  
Briggs, Peter  
McNally, Kevin  
O'Mahony, Michael P.  
Uri
http://hdl.handle.net/10197/10874
Date Issued
2015
Date Available
2019-07-10T11:01:13Z
Abstract
Modern web search engines have come to dominate how millions of people find the information that they are looking for online. While the sheer scale and success of the leading search engines is a testimony to the scientific and engineering progress that has been made over the last two decades, mainstream search is not without its challenges. Mainstream search engines continue to provide a largely one-size-fits-all service to their user-base, ultimately limiting the relevance of their result-lists. And they have only very recently begun to consider how the rise of the social web may support novel approaches to search and discovery, or how such signals can be used to inform relevance. In this chapter we will explore recent research which aims to do just that: to make web search a more personal and collaborative experience and to leverage important information such as the reputation of searchers during result-ranking. In short we look towards a more social future for mainstream search.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Centre for Data Analytics
Type of Material
Book Chapter
Publisher
Springer
Start Page
569
End Page
608
Copyright (Published Version)
2015 Springer
Subjects

Search engine

Recommender system

User profile

Relevance feedback

Reputation system

DOI
10.1007/978-1-4899-7637-6_17
Language
English
Status of Item
Peer reviewed
Journal
Ricci, F., Rokach, L., Shapira, B. (eds.). Recommender Systems Handbook
ISBN
978-1-4899-7637-6
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Collaboration,Reputation and Recommender systems in social web search.pdf

Size

1.11 MB

Format

Adobe PDF

Checksum (MD5)

5d419692f953935d56a37738cd6e79c7

Owning collection
Insight 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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