Exploring the similarity space
ACM SIGIR Forum
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Fourier Domain Scoring with Document Structure Consideration
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WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
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An information retrieval model based on discrete fourier transform
IRFC'10 Proceedings of the First international Information Retrieval Facility conference on Adbances in Multidisciplinary Retrieval
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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Abstract--Current document retrieval methods use a vector space similarity measure to give scores of relevance to documents when related to a specific query. The central problem with these methods is that they neglect any spatial information within the documents in question. We present a new method, called Fourier Domain Scoring (FDS), which takes advantage of this spatial information, via the Fourier transform, to give a more accurate ordering of relevance to a document set. We show that FDS gives an improvement in precision over the vector space similarity measures for the common case of Web like queries, and it gives similar results to the vector space measures for longer queries.