Information Retrieval (IR) refers to finding information from large amounts of text, and is among the most useful technologies for overcoming information overload. For example, Web search engines are now essential tools for everyone to find information on the Web. Existing IR systems are inherently non-optimal because the retrieval decision is made primarily based on the current query and the document collection without considering information about the user and search context. This project seeks to eliminate this limitation of the existing retrieval methods and formally develop a new retrieval paradigm called user-centered adaptive information retrieval (UCAIR), in which user information and search context are both exploited to improve retrieval performance. This project includes research on: (1) developing a new UCAIR framework based on Bayesian decision theory; (2) developing new language models to exploit user information and search context to improve retrieval accuracy; (3) developing new retrieval methods to optimize the long-term retrieval utility over an entire retrieval session; (4) developing new retrieval methods to leverage user similarities to better infer one particular user's information need based on information about other similar users; and (5) developing prototype UCAIR systems for searching the Web and bioinformatics literature. The project will advance the state of the art of IR through developing a unified formal framework for UCAIR, a variety of retrieval methods for exploiting user information and search context to improve retrieval accuracy, which will directly lead to more effective information retrieval applications in all domains. The research results will also enhance the current IR curricula, improving education of information technology workforce. The project Web site http://sifaka.cs.uiuc.edu/ucair/ will be used for research results dissemination.
