Support for browsing in an intelligent text retrieval system
International Journal of Man-Machine Studies
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SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
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An evaluation of automatic query expansion in an online library catalogue
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Automatic hypertext construction
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Advantages of query biased summaries in information retrieval
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Depth- and breadth-first processing of search result lists
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Using similarity links as shortcuts to relevant web pages
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Towards a unified approach to document similarity search using manifold-ranking of blocks
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Evaluating an associative browsing model for personal information
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Model for simulating result document browsing in focused retrieval
Proceedings of the 4th Information Interaction in Context Symposium
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Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Search systems have for some time provided users with the ability to request documents similar to a given document. Interfaces provide this feature via a link or button for each document in the search results. We call this feature find-similar or similarity browsing. We examined find-similar as a search tool, like relevance feedback, for improving retrieval performance. Our investigation focused on find-similar's document-to-document similarity, the reexamination of documents during a search, and the user's browsing pattern. Find-similar with a query-biased similarity, avoiding the reexamination of documents, and a breadth-like browsing pattern achieved a 23% increase in the arithmetic mean average precision and a 66% increase in the geometric mean average precision over our baseline retrieval. This performance matched that of a more traditionally styled iterative relevance feedback technique.