leveraging a common representation for personalized search and summarization in a medical digital library
Despite the large amount of online medical literature, it can be difficult for clinicians to find relevant information at the point of patient care. In this paper, we present techniques to personalize the results of search, making use of the online patient record as a sophisticated, pre-existing user model. Our work in PERSIVAL , a medical digital library, includes methods for re-ranking the results of search to prioritize those that better match the patient record. It also generates summaries of the re-ranked results which highlight information that is relevant to the patient under the physician's care. We focus on the use of a common representation for the articles returned by search and the patient record which facilitates both the re-ranking and the summarization tasks. This common approach to both tasks has a strong positive effect on the ability to personalize information.
representation personality personalized search search summarization digital libraries online relevance informal point paper use records user model method matching posit effect ranking