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John O’Donovan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

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TIST Journal 2012 Journal Article

TopicNets

  • Brynjar Gretarsson
  • John O’Donovan
  • Svetlin Bostandjiev
  • Tobias Höllerer
  • Arthur Asuncion
  • David Newman
  • Padhraic Smyth

We present TopicNets, a Web-based system for visual and interactive analysis of large sets of documents using statistical topic models. A range of visualization types and control mechanisms to support knowledge discovery are presented. These include corpus- and document-specific views, iterative topic modeling, search, and visual filtering. Drill-down functionality is provided to allow analysts to visualize individual document sections and their relations within the global topic space. Analysts can search across a dataset through a set of expansion techniques on selected document and topic nodes. Furthermore, analysts can select relevant subsets of documents and perform real-time topic modeling on these subsets to interactively visualize topics at various levels of granularity, allowing for a better understanding of the documents. A discussion of the design and implementation choices for each visual analysis technique is presented. This is followed by a discussion of three diverse use cases in which TopicNets enables fast discovery of information that is otherwise hard to find. These include a corpus of 50,000 successful NSF grant proposals, 10,000 publications from a large research center, and single documents including a grant proposal and a PhD thesis.

Details DOI

IJCAI Conference 2005 Conference Paper

Trust No One: Evaluating Trust-based Filtering for Recommenders

  • John O’Donovan
  • Barry

To be successful recommender systems must gain the trust of users. To do this they must demonstrate their ability to make reliable predictions. We argue that collaborative filtering recommendation algorithms can benefit from explicit models of trust to inform their predictions. We present one such model of trust along with a cost-benefit analysis that focuses on the classical trade-off that exists between recommendation coverage and prediction accuracy.

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