Buzzer : online real-time topical news article and source recommender

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Title: Buzzer : online real-time topical news article and source recommender
Authors: Phelan, Owen
McCarthy, Kevin
Smyth, Barry
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Date: Aug-2009
Online since: 2009-11-27T14:59:50Z
Abstract: The significant growth of media and user-generated content online has allowed for the widespread adoption of recommender systems due to their proven ability to reduce the workload of a user and personalise content. In this paper, we describe our prototype system called Buzzer, which harnesses real-time micro-blogging activity, such as Twitter, as the basis for promoting personalised content, such as news articles, from RSS feeds. We also introduce several new features, that include a technique for recommending community articles from the pooled resources of all system users and also a mechanism for recommending source RSS feeds to which the user does not subscribe.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): Springer-Verlag Berlin Heidelberg 2010
Subject LCSH: Recommender systems (Information filtering)
Social media
Web personalization
User-generated content
DOI: 10.1007/978-3-642-17080-5_27
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Language: en
Status of Item: Not peer reviewed
Is part of: L. Coyle, J. Freyne (ed.s). Artificial Intelligence and Cognitive Science : 20th Irish Conference, AICS 2009 Dublin, Ireland, August 19-21, 2009 : Revised Selected Papers, LNAI 6206
Conference Details: Presentation at the 20th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 09), 19th-21st August 2009, Dublin
ISBN: 978-3-642-17079-9
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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