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• W2783153390 abstract "Search has become a fundamental life activity. An individual looks for meaningful information not only for work-related requests but also for psychological and social satisfaction. A traditional query-result paradigm is able to deliver sensibly for short, navigational and close information requests. It is not always adequate, particularly when user is not familiar with the syntax and semantics of the data, and worse when the user is uncertain of his information needs. Such as the case in discovery-oriented applications e.g. scientific data, genomics, health data etc.. User’s intention additionally helps to navigate through the unknown data, formulate queries and find desired information. Also, user’s initial search aims and intentions evolve as new information is encountered. To guide users in navigating large datasets in discovery-related applications, we proposed an interactive exploratory search system with visualizations and interactive features to guide the user in recognizing the relationship between query and results retrieved. Proposed system incorporates explicit user feedback to re-rank results according to the user’s intent, contrary to traditional systems predefined relevance norms. The usage pattern and usability of various interactions are measured with the help of a suggested assessment framework. Proposed ESS outperforms present list based systems in lowering the cognitive burden, swift interactions, rapid and improved convergence towards search goals." @default.
• W2783153390 created "2018-01-26" @default.
• W2783153390 creator A5027196381 @default.
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• W2783153390 date "2018-01-01" @default.
• W2783153390 modified "2023-09-25" @default.
• W2783153390 title "Learn-As-You-Go : Feedback-Driven Result Ranking and Query Refinement for Interactive Data Exploration" @default.
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• W2783153390 doi "https://doi.org/10.1016/j.procs.2017.12.071" @default.
• W2783153390 hasPublicationYear "2018" @default.
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