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Phelan, Owen
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Phelan, Owen
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Phelan, Owen
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Now showing 1 - 6 of 6
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Publication
Towards a Novel and Timely Search and Discovery System Using the Real-Time Social Web
2013-04-06, Phelan, Owen, McCarthy, Kevin, Smyth, Barry
The world of web search is changing. Mainstream search engines like Google and Bing are adding social signals to conventional query-based services while social networks like Twitter and Facebook are adding query-based search to sharing-based services. Our search and discovery system, Yokie, harnesses the wisdom of the crowd of communities of Twitter users to create indexes of proto-content (or recently shared content) that is typically not yet indexed by mainstream search engines. The system includes an architecture [13] for a range of contextual queries and ranking strategies beyond standard relevance. In this paper, we focus on evaluating Yokies ability to retrieve timely, relevant and exclusive results with which users interacted and found useful, compared to other standard web services.
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Buzzer : online real-time topical news article and source recommender
2009-08, Phelan, Owen, McCarthy, Kevin, Smyth, Barry
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.
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The Ambient Calendar
2008-08, Phelan, Owen, Coyle, Lorcan, Stevenson, Graeme, Neely, Steve
It is becoming difficult to convey information from an everincreasing
number of digital sources to users in a condensed and meaningful
way. This growth has particularly occurred with peripheral information
sources. These are of general interest to users, but do no require
or typically command constant focus or attention. Examples include
weather, stock data, blogs, and calendars. Ambient Displays present information
unobtrusively in an intelligent fashion using abstract visual
cues and metaphors and have the possibility of acting as a complement
to information filtering systems. We describe the implementation of an
ambient display that contains elements representing time, weather, public
transport departure times, and the proximity of friends. An initial
impact study was undertaken and found a high sense of usefulness and
curiosity in the finished application and in the field as a whole.
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Publication
Using Twitter to recommend real-time topical news
2009-10, Phelan, Owen, McCarthy, Kevin, Smyth, Barry
Recommending news stories to users, based on their preferences,has long been a favourite domain for recommender systems research. In this paper, we describe a novel approach
to news recommendation that harnesses real-time micro-blogging activity, from a service such as Twitter, as the basis for promoting news stories from a user's favourite RSS feeds. A preliminary evaluation is carried out on an
implementation of this technique that shows promising results.
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On using the real-time web for news recommendation & discovery
2011-03-28, Phelan, Owen, McCarthy, Kevin, Bennett, Mike, Smyth, Barry
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.
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Publication
Terms of a feather : content-based news discovery and recommendation using Twitter
2011-04-19, Phelan, Owen, McCarthy, Kevin, Smyth, Barry, Bennett, Mike
User-generated content has dominated the web’s recent growth and today the so-called real-time web provides us with unprecedented access to the real-time opinions, views, and ratings of millions of users. For example, Twitter’s 200m+ users are generating in the region of 1000+ tweets per second. In this work, we propose that this data can be harnessed as a useful source of recommendation knowledge. We describe a social news service called Buzzer that is capable of adapting to the conversations that are taking place on Twitter to ranking personal RSS subscriptions. This is achieved by a content-based approach of mining trending terms from both the public Twitter timeline and from the timeline of tweets published by a user’s own Twitter friend subscriptions. We also present results of a live-user evaluation which demonstrates how these ranking strategies can add better item filtering and discovery value to conventional recency-based RSS ranking techniques.