A Case Study of Collaboration and Reputation in Social Web Search.

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dc.contributor.author McNally, Kevin
dc.contributor.author O'Mahony, Michael P.
dc.contributor.author Coyle, Maurice
dc.contributor.author Briggs, Peter
dc.contributor.author Smyth, Barry
dc.date.accessioned 2012-11-23T15:42:47Z
dc.date.available 2012-11-23T15:42:47Z
dc.date.copyright 2011 ACM en
dc.date.issued 2011-10
dc.identifier.citation ACM Transactions on Intelligent Systems and Technology en
dc.identifier.uri http://hdl.handle.net/10197/3913
dc.description.abstract Although collaborative searching is not supported by mainstream search engines, recent research has high- lighted the inherently collaborative nature of many web search tasks. In this paper, we describe HeyStaks (www.heystaks.com), a collaborative web search framework that is designed to complement mainstream search engines. At search time, HeyStaks learns from the search activities of other users and leverages this information to generate recommendations based on results that others have found relevant for similar searches. The key contribution of this paper is to extend the HeyStaks social search model by considering the search expertise, or reputation, of HeyStaks users and using this information to enhance the result recommendation process. In particular, we propose a reputation model for HeyStaks users that utilises the implicit collaboration events that take place between users as recommendations are made and selected. We describe a live-user trial of HeyStaks that demonstrates the relevance of its core recommendations and the ability of the reputation model to further improve recommendation quality. Our findings indicate that incorporating reputation into the recommendation process further improves the relevance of HeyStaks recommendations by up to 40%. en
dc.description.sponsorship Science Foundation Ireland en
dc.language.iso en en
dc.publisher ACM en
dc.relation.requires CLARITY Research Collection en
dc.rights © ACM, 2011 This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Intelligent Systems and Technology (TIST), Volume 3 Issue 1, October 2011 http://dx.doi.org/10.1145/2036264.2036268 en
dc.subject Algorithms en
dc.subject Experimentation en
dc.subject Security en
dc.subject Trust en
dc.subject Reputation en
dc.subject Social Search en
dc.subject HeyStaks en
dc.subject.lcsh Web co-browsing en
dc.subject.lcsh Internet searching en
dc.subject.lcsh Recommender systems (Information filtering) en
dc.subject.lcsh Information behavior en
dc.title A Case Study of Collaboration and Reputation in Social Web Search. en
dc.type Journal Article en
dc.internal.availability Full text available en
dc.status Peer reviewed en
dc.identifier.volume 3 en
dc.identifier.issue 1 en
dc.identifier.doi 10.1145/2036264.2036268
dc.neeo.contributor McNally|Kevin|aut|
dc.neeo.contributor O'Mahony|Michael P.|aut|
dc.neeo.contributor Coyle|Maurice|aut|
dc.neeo.contributor Briggs|Peter|aut|
dc.neeo.contributor Smyth|Barry|aut|
dc.description.admin DG 22/11/12 en
dc.description.admin Names JG 2012-11-22 en
dc.description.admin This item was created in a template provided by the publisher -- attached copy is the pre-published version. JG 2012-11-20 en


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