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- W87579370 abstract "Previous chapter Next chapter Full AccessProceedings Proceedings of the 2007 SIAM International Conference on Data Mining (SDM)Rank Aggregation for Similar ItemsD. SculleyD. Sculleypp.587 - 592Chapter DOI:https://doi.org/10.1137/1.9781611972771.66PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract The problem of combining the ranked preferences of many experts has gained renewed importance in data mining, and information retrieval applications. Effective rank aggregation becomes difficult in real-world situations in which the rankings are noisy, incomplete, or even disjoint. We address these difficulties by extending several standard methods of rank aggregation to consider similarity between items in the various ranked lists, in addition to their rankings. We establish evaluation criteria for these algorithms by extending previous definitions of distance between ranked lists to include the role of similarity between items. Experimental results show that this approach improves the performance of several standard rank aggregation methods, particularly when used with noisy, incomplete rankings. Previous chapter Next chapter RelatedDetails Published:2007ISBN:978-0-89871-630-6eISBN:978-1-61197-277-1 https://doi.org/10.1137/1.9781611972771Book Series Name:ProceedingsBook Code:PR127Book Pages:xiv + 648" @default.
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- W87579370 title "Rank Aggregation for Similar Items" @default.
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