On using the real-time web for news recommendation & discovery

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Title: On using the real-time web for news recommendation & discovery
Authors: Phelan, Owen
McCarthy, Kevin
Bennett, Mike
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/2954
Date: 28-Mar-2011
Abstract: In this work we propose that the high volumes of data on real-time networks like Twitter can be harnessed as a useful source of recommendation knowledge. We describe Buzzer, a news recommendation system that is capable of adapting to the conversations that are taking place on Twitter. Buzzer uses a content-based approach to ranking RSS news stories by mining trending terms from both the public Twitter timeline and from the timeline of tweets generated by a user’s own social graph (friends and followers). We also describe the result of a live-user trial which demonstrates how these ranking strategies can add value to conventional RSS ranking techniques, which are largely recency-based.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2011 The authors
Keywords: Social recommendationNews recommendationContent-based recommendationRealtime recommendationTwitter
Subject LCSH: Recommender systems (Information filtering)
Web 2.0
Social media
Web personalization
DOI: 10.1145/1963192.1963245
Language: en
Status of Item: Peer reviewed
Is part of: WWW '11 Proceedings of the 20th international conference companion on World wide web
Conference Details: Presented at the 20th International World Wide Web Conference, WWW 2011, Hyderabad, India, March 28 - April 1, 2011
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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