J. O'Donovan

Publications
Influence

J. O'Donovan, B. Smyth
Computer Science
IUI '05
10 January 2005

In this paper we suggest that the traditional emphasis on user similarity may be overstated. Expand

TasteWeights: a visual interactive hybrid recommender system

Svetlin Bostandjiev, J. O'Donovan, Tobias Höllerer
Computer Science
RecSys '12
9 September 2012

This paper presents an interactive hybrid recommendation system that generates item predictions from multiple social and semantic web resources, such as Wikipedia, Facebook, and Twitter, using hybrid techniques from traditional recommender system literature. Expand

Inspectability and control in social recommenders

Bart P. Knijnenburg, Svetlin Bostandjiev, J. O'Donovan, A. Kobsa
Computer Science
RecSys '12
9 September 2012

We performed an online user experiment (N=267) with a Facebook music recommender system that gives users control over the recommendations, and explains how they came about. Expand

Modeling topic specific credibility on twitter

B. Kang, J. O'Donovan, Tobias Höllerer
Computer Science
IUI '12
14 February 2012

This paper presents and evaluates three computational models for recommending credible topic-specific information in Twitter. Expand

TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling

Brynjar Gretarsson, J. O'Donovan, +4 authors Padhraic Smyth
Computer Science
TIST
1 February 2012

We present TopicNets, a Web-based system for visual and interactive analysis of large sets of documents using statistical topic models. Expand

PeerChooser: visual interactive recommendation

J. O'Donovan, B. Smyth, Brynjar Gretarsson, Svetlin Bostandjiev, Tobias Höllerer
Computer Science
CHI
6 April 2008

We present PeerChooser, a collaborative recommender system with an interactive graphical explanation interface. Expand

Credibility in Context: An Analysis of Feature Distributions in Twitter

J. O'Donovan, B. Kang, G. Meyer, Tobias Höllerer, Sibel Adali
Computer Science
International Conference on Privacy, Security…
3 September 2012

In this paper, we focus on an analysis that highlights the utility of the individual features in Twitter such as hash tags, retweets and mentions for predicting credibility. Expand

Moodplay: Interactive music recommendation based on Artists' mood similarity

I. Andjelkovic, D. Parra, J. O'Donovan
Computer Science
Int. J. Hum. Comput. Stud.
2019

This paper introduces MoodPlay, an interactive music-artists recommender system which integrates content and mood-based filtering in a novel interface. Expand

Believe it or not? Analyzing information credibility in microblogs

B. Kang, Tobias Höllerer, J. O'Donovan
Computer Science
IEEE/ACM International Conference on Advances in…
25 August 2015

This paper identifies and evaluates key factors that influence credibility perception in microblogs. Expand

SmallWorlds: Visualizing Social Recommendations

Brynjar Gretarsson, J. O'Donovan, Svetlin Bostandjiev, C. Hall, Tobias Höllerer
Computer Science
Comput. Graph. Forum
1 June 2010

We present SmallWorlds, a visual interactive graph‐based interface that allows users to specify, refine and build item‐preference profiles in a variety of domains. Expand

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