ACM Transactions on Information Systems (TOIS)
Automatic feature selection in the markov random field model for information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Boilerplate detection using shallow text features
Proceedings of the third ACM international conference on Web search and data mining
Mining the blogosphere for top news stories identification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
News article ranking: leveraging the wisdom of bloggers
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Learning models for ranking aggregates
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Information Retrieval on the Blogosphere
Foundations and Trends in Information Retrieval
Ranking news events by influence decay and information fusion for media and users
Proceedings of the 21st ACM international conference on Information and knowledge management
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Newspaper websites and news aggregators rank news stories by their newsworthiness in real-time for display to the user. Recent work has shown that news stories can be ranked automatically in a retrospective manner based upon related discussion within the blogosphere. However, it is as yet undetermined whether blogs are sufficiently fresh to rank stories in real-time. In this paper, we propose a novel learning to rank framework which leverages current blog posts to rank news stories in a real-time manner. We evaluate our proposed learning framework within the context of the TREC Blog track top stories identification task. Our results show that, indeed, the blogosphere can be leveraged for the realtime ranking of news, including for unpredictable events. Our approach improves upon state-of-the-art story ranking approaches, outperforming both the best TREC 2009/2010 systems and its single best performing feature.