{"id":158944,"date":"2010-04-26T00:00:00","date_gmt":"2010-04-26T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/b-bit-minwise-hashing\/"},"modified":"2018-10-16T21:12:37","modified_gmt":"2018-10-17T04:12:37","slug":"b-bit-minwise-hashing","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/b-bit-minwise-hashing\/","title":{"rendered":"b-Bit Minwise Hashing"},"content":{"rendered":"
This paper establishes the theoretical framework of b-bit minwise hashing. The original minwise hashing method has become a standard technique for estimating set similarity (e.g., resemblance) with applications in information retrieval, data management, computational advertising, etc.<\/p>\n
By only storing b bits of each hashed value (e.g., b = 1 or 2), we gain substantial advantages in terms of storage space. We prove the basic theoretical results and provide an unbiased estimator of the resemblance for any b. We demonstrate that, even in the least favorable scenario, using b = 1 may reduce the storage space at least by a factor of 21.3 (or 10.7) compared to b = 64 (or b = 32), if one is interested in resemblance > 0.5. Our theoretical results are validated using a proprietary collection of 106<\/sup> news articles and a public dataset of 300.000 articles.<\/p>\n<\/div>\n <\/p>\n","protected":false},"excerpt":{"rendered":" This paper establishes the theoretical framework of b-bit minwise hashing. The original minwise hashing method has become a standard technique for estimating set similarity (e.g., resemblance) with applications in information retrieval, data management, computational advertising, etc. By only storing b bits of each hashed value (e.g., b = 1 or 2), we gain substantial advantages […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Association for Computing Machinery, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Nineteenth International World Wide Web Conference (WWW 2010)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"Copyright \u00a9 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and\/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org. 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