Towards a reputation-based model of social web search
|Title:||Towards a reputation-based model of social web search||Authors:||McNally, Kevin
O'Mahony, Michael P.
|Permanent link:||http://hdl.handle.net/10197/2034||Date:||7-Feb-2010||Abstract:||While web search tasks are often inherently collaborative in nature, many search engines do not explicitly support collaboration during search. In this paper, we describe HeyStaks (www.heystaks.com), a system that provides a novel approach to collaborative web search. Designed to work with mainstream search engines such as Google, HeyStaks supports searchers by harnessing the experiences of others as the basis for result recommendations. Moreover, a key contribution of our work is to propose a reputation system for HeyStaks to model the value of individual searchers from a result recommendation perspective. In particular, we propose an algorithm to calculate reputation directly from user search activity and we provide encouraging results for our approach based on a preliminary analysis of user activity and reputation scores across a sample of HeyStaks users.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||Association for Computing Machinery||Copyright (published version):||2010 the Association for Computing Machinery||Keywords:||Collaborative web search;Reputation model;HeyStaks||Subject LCSH:||Internet searching--Software
Online social networks
Recommender systems (Information filtering)
|DOI:||10.1145/1719970.1719996||Language:||en||Status of Item:||Peer reviewed||Is part of:||IUI 2010 : proceeding of the 14th international conference on Intelligent user interfaces||Conference Details:||2010 International Conference on Intelligent User Interfaces (IUI 2010), Hong Kong, China, 7-10 February 2010|
|Appears in Collections:||CLARITY Research Collection|
Computer Science Research Collection
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