Modeling user and result reputation in collaborative web search

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Title: Modeling user and result reputation in collaborative web search
Authors: McNally, Kevin
O'Mahony, Michael P.
Smyth, Barry
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Date: 31-Aug-2011
Abstract: Employing collaborative recommendation techniques allows for personalization of search results in social web search. If recommendations arise from past user activity, the expertise of those users driving the recommendation process can play an important role when it comes to ensuring recommendation quality. Hence the reputation of users is important, in addition to result relevance as traditionally considered in web search. In this paper we explore this concept of reputation; specifically, investigating how reputation can enhance the recommendation engine at the core of the HeyStaks social search utility. We evaluate a number of different reputation models in the context of the HeyStaks system, and demonstrate how incorporating reputation into the recommendation process can enhance the relevance of results recommended by HeyStaks.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Intelligent Systems Research Centre
Keywords: SensorsSocial SearchRelevanceReputationHeyStaks
Subject LCSH: Web co-browsing--Software
Internet searching--Software
Recommender systems (Information filtering)
Online social networks
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Language: en
Status of Item: Peer reviewed
Is part of: AICS 2011 : Proceedings of the 22nd Irish Conference on Artificial Intelligence and Cognitive Science : 31 August - 2 September, 2011 : University of Ulster - Magee
Conference Details: Paper presented at the 22nd Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2011), University of Ulster, Northern Ireland, 31 August - 2 September, 2011
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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