Learning SVM Ranking Functions from User Feedback Using Document Metadata and Active Learning in the Biomedical Domain

Information overload is a well-known problem facing biomedical professionals. MEDLINE, the biomedical bibliographic database, adds hundreds of articles daily to the millions already in its collection. This overload is exacerbated by the lack of relevance-based ranking for search results, as well as disparate levels of search skill and domain experience of professionals using systems designed to search MEDLINE. We propose to address these problems through learning ranking functions from user relevance feedback. Using simple active learning techniques, ranking functions can be learned using a fraction of the available data which approach the performance of functions learned using all available data. Furthermore, ranking functions learned using metadata features from the Medical Subject Heading (MeSH) terms associated with MEDLINE citations greatly outperform functions learned using textual features. An in-depth investigation is made into the effect of a number of variables in the ranking round, while further investigation is made into peripheral issues such as users providing inconsistent data.