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Beyond Keyword Search: Discovering Relevant Scientific Literature

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If you have a question about this talk, please contact Zoubin Ghahramani.

In scientific research, it is often difficult to express information needs as simple keyword queries. In this talk, I will present a more natural way of searching for relevant scientific literature. Rather than a string of keywords, we define a query as a small set of papers deemed relevant to the research task at hand. By optimizing a submodular objective function based on a fine-grained notion of influence between documents, our approach efficiently selects a set of highly relevant articles, while maintaining diversity in the result set. Moreover, as scientists trust some authors more than others, we are able to personalize results to individual preferences.

In a user study, researchers found the papers recommended by our method to be more useful, trustworthy and diverse than those selected by popular alternatives, such as Google Scholar and a state-of-the-art topic modeling approach.

This is joint work with Carlos Guestrin, and is to appear at KDD 2011 in August. (Link to the paper can be found here.)

This talk is part of the Machine Learning @ CUED series.

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