Context-sensitive and task-oriented adaptive information retrieval

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Using Google is easy; finding information using Google is another matter. Over the past decades, significant progress has been made in Information Retrieval (IR). However, many challenges remain. First, most Web search engines take a short text query as input and output a ranked list of documents. The retrieval decision is made primarily based on the current query and document collection. For example, a person may use "IRIX" to mean Information Retrieval in Context at one time, but IRIX operating systems at another time. It is impossible for the current search engines to distinguish these two cases because the user's search history and context are not considered. Second, IR is, in general, an interactive process. With the current document-centered retrieval paradigm, interactive retrieval is treated as a sequence of independent simple retrieval decision-making steps. However, it has been brought into attention that analysis of task-oriented user sessions provides useful insight into the query behavior of the users. Third, most of present IR systems including general search engines (e.g. Google) and scientific literature search engines (e.g. PubMed) use keywords to query and index documents, which provides little semantic context for the understanding of the user's information needs. Thus, the integration of semantic context according to the user's information need and the user's understanding of the documents in the collection into IR systems will definitely improve the IR performance. However, how to encode and use semantic context evidence for the benefit of retrieval is a difficult problem.Our objective is to overcome the limitations of the existing retrieval methods and formally develop a new retrieval paradigm, in which user information and search context are both exploited in the retrieval decision process. In particular, (1) we will develop a personalized theoretical retrieval model for capturing user information and search context; (2) we will develop a novel task-based retrieval model that optimizes the long-term retrieval utility over an entire retrieval session.