On using the real-time web for news recommendation & discovery
|Title:||On using the real-time web for news recommendation & discovery||Authors:||Phelan, Owen
|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 recommendation;News recommendation;Content-based recommendation;Realtime recommendation;Twitter||Subject LCSH:||Recommender systems (Information filtering)
|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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