{"id":161610,"date":"2011-08-01T00:00:00","date_gmt":"2011-08-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/theory-and-applications-of-b-bit-minwise-hashing\/"},"modified":"2018-10-16T22:10:21","modified_gmt":"2018-10-17T05:10:21","slug":"theory-and-applications-of-b-bit-minwise-hashing","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/theory-and-applications-of-b-bit-minwise-hashing\/","title":{"rendered":"Theory and Applications of b-Bit Minwise Hashing"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Efficient (approximate) computation of set similarity in very large datasets is a common task with many applications in information retrieval and data management. One common approach for this task is minwise hashing. This paper describes b-bit minwise hashing, which can provide an order of magnitude improvements in storage requirements and computational overhead over the original scheme in practice.<\/p>\n<p>We give both theoretical characterizations of the performance of the new algorithm as well as a practical evaluation on large real-life datasets and show that these match very closely. Moreover, we provide a detailed comparison with other important alternative techniques proposed for estimating set similarities. Our technique yields a very simple algorithm and can be realized with only minor modifications to the original minwise hashing scheme.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Efficient (approximate) computation of set similarity in very large datasets is a common task with many applications in information retrieval and data management. One common approach for this task is minwise hashing. This paper describes b-bit minwise hashing, which can provide an order of magnitude improvements in storage requirements and computational overhead over the original [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13555],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-161610","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"Communications of the 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