Models of web page reputation in social search

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Title: Models of web page reputation in social search
Authors: McNally, Kevin
O'Mahony, Michael P.
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/3507
Date: 9-Oct-2011
Abstract: To date web search has been a solitary experience for the end-user, despite the fact that recent studies highlight the potential for collaboration that is inherent in many search tasks and scenarios. As a result, researchers have begun to explore the potential for a more collaborative approach to web search, one in which the search actions of other users can influence the results returned. In this context, the expertise of other users plays an important role when it comes to ensuring the quality of recommendations that arise from their actions. The reputation of these users is important in collaborative and social search tasks, much as relevance is vital in conventional web search. In this paper we examine this concept of reputation in collaborative and social search contexts. We describe a number of different reputation models and evaluate them in the context of a particular social search service. Our results highlight the potential for reputation to improve the quality of recommendations that arise from the activities of other searchers.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2011 IEEE
Keywords: Recommender systemsReputationSocial search
Subject LCSH: Web co-browsing
Recommender systems (Information filtering)
Online social networks
DOI: 10.1109/PASSAT/SocialCom.2011.175
Language: en
Status of Item: Peer reviewed
Is part of: Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom) [proceedings]
Conference Details: Paper presented at the Third IEEE International Conference on Social Computing (SocialCom2011), MIT, Boston, USA, 9-11 October 2011
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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