Huang, Xiangji2014-07-312014-07-312014-04-242014-07-28http://hdl.handle.net/10315/27704In most of the traditional information retrieval (IR) models, the independent relevance assumption is taken, which assumes the relevance of a document is independent of other documents. However, the pitfall of this is the high redundancy and low diversity of retrieval result. This has been seen in many scenarios, especially in biomedical IR, where the information need of one query may refer to different aspects. Promoting diversity in IR takes the relationship between documents into account. Unlike previous studies, we tackle this problem in the learning to rank perspective. The main challenges are how to find salient features for biomedical data and how to integrate dynamic features into the ranking model. To address these challenges, Wikipedia is used to detect topics of documents for generating diversity biased features. A combined model is proposed and studied to learn a diversified ranking result. Experiment results show the proposed method outperforms baseline models.enAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Information technologyComputer scienceElectronic Thesis or Dissertation2014-07-28WikipediaBiomedical Information RetrievalLearning to RankDiversity Information Retrieval