报告题目：Promoting Diversity in Ranking Through Bayesian Learning
Here I propose a Bayesian learning approach to promoting diversity for information retrieval in biomedicine and a re-ranking model to improve retrieval performance in the biomedical domain. First, the re-ranking model computes the maximum posterior probability of the hidden property corresponding to each retrieved passage. Then it iteratively groups the passages into subsets according to their properties. Finally, these passages are re-ranked from the subsets as the output. There is no need for the proposed method to use any external biomedical resource. The Bayesian learning approach is evaluated by conducting extensive experiments on the TREC 2004-2007 Genomics data sets. The experimental results show the effectiveness of the proposed Bayesian learning approach for promoting diversity in ranking for biomedical information retrieval on four years TREC data sets.
报告人简介：Qinmin Hu is a doctoral candidate at the department of Computer Science & Engineering in York University, Canada (expected completion by Feb. 2013). She received a B.Sc degree in Mathematics and Statistics from Wuhan University, China and a M.Sc degree in Computer Science in York University. Her research interests primarily lie in large-scale information systems, information retrieval, crowdsourcing, Web mining and data mining. In particular, the research activities focus on term association, aspect search, entity search on text, image and video. Please find more details at http://www.cse.yorku.ca/~vhu/or check her CV.