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Sources of Evidence for Interactive Table Completion

An important question in interactive information retrieval (IIR) is: How can we support searchers with specific types of search tasks? We describe an auxiliary support tool referred to as the "Matrix''. The Matrix tool was designed to support searchers with comparative search tasks, which require comparing items along different dimensions. The Matrix was designed as a grid of rows and columns representing the items and dimensions related to a comparative task. The Matrix was integrated with a custom-built search interface, which allowed users to search for information and drag-and-drop relevant passages directly into cells in the Matrix. We investigate the following general question: Given a partially completed Matrix, can a system automatically populate empty cells in the Matrix with relevant passages? To this end, we conducted two crowdsourced studies in which participants were assigned comparative tasks and asked to use our system (integrated search interface + Matrix) to populate every cell in the Matrix. After gathering this data, we evaluated machine-learned models for ranking passages in response to an empty Matrix cell and partially completed Matrix. We address two research questions: (RQ1) What are useful types of features for this predictive task? and (RQ2) How does performance vary based on the level of Matrix completion? We view our research as a step towards designing support tools that: (1) help users organize information while searching and (2) can autocomplete search tasks by exploiting the task structure and a searcher's partial solution.