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| dc.contributor.author | Phelan, Owen | |
| dc.contributor.author | McCarthy, Kevin | |
| dc.contributor.author | Bennett, Mike | |
| dc.contributor.author | Smyth, Barry | |
| dc.date.accessioned | 2011-05-26T11:44:12Z | |
| dc.date.available | 2011-05-26T11:44:12Z | |
| dc.date.copyright | 2011 The authors | en |
| dc.date.issued | 2011-03-28 | |
| dc.identifier.isbn | 978-1-4503-0637-9 | |
| dc.identifier.uri | http://hdl.handle.net/10197/2954 | |
| dc.description | Presented at the 20th International World Wide Web Conference, WWW 2011, Hyderabad, India, March 28 - April 1, 2011 | en |
| dc.description.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. | en |
| dc.description.sponsorship | Science Foundation Ireland | en |
| dc.description.uri | Conference details | en |
| dc.description.uri | http://www.www2011india.com/ | en |
| dc.format.extent | 526456 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | ACM | en |
| dc.relation.ispartof | WWW '11 Proceedings of the 20th international conference companion on World wide web | en |
| dc.relation.requires | CLARITY Research Collection | en |
| dc.rights | This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 20th international conference companion on World wide web available at http://doi.acm.org/10.1145/1963192.1963245 | en |
| dc.subject | Social recommendation | en |
| dc.subject | News recommendation | en |
| dc.subject | Content-based recommendation | en |
| dc.subject | Realtime recommendation | en |
| dc.subject | en | |
| dc.subject.lcsh | Recommender systems (Information filtering) | en |
| dc.subject.lcsh | Web 2.0 | en |
| dc.subject.lcsh | Social media | en |
| dc.subject.lcsh | Web personalization | en |
| dc.subject.lcsh | en | |
| dc.title | On using the real-time web for news recommendation & discovery | en |
| dc.type | Conference Publication | en |
| dc.internal.availability | Full text available | en |
| dc.internal.webversions | http://dx.doi.org/10.1145/1963192.1963245 | en |
| dc.status | Peer reviewed | en |
| dc.identifier.doi | 10.1145/1963192.1963245 | |
| dc.neeo.contributor | Phelan|Owen|aut| | en |
| dc.neeo.contributor | McCarthy|Kevin|aut| | en |
| dc.neeo.contributor | Bennett|Mike|aut| | en |
| dc.neeo.contributor | Smyth|Barry|aut| | en |
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