Efficient estimation for high similarities using odd sketches

Michael Mitzenmacher, Rasmus Pagh, Ninh Dang Pham

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Abstrakt

Estimating set similarity is a central problem in many computer applications. In this paper we introduce the Odd Sketch, a compact binary sketch for estimating the Jaccard similarity of two sets. The exclusive-or of two sketches equals the sketch of the symmetric difference of the two sets. This means that Odd Sketches provide a highly space-efficient estimator for sets of high similarity, which is relevant in applications such as web duplicate detection, collaborative filtering, and association rule learning. The method extends to weighted Jaccard similarity, relevant e.g. for TF-IDF vector comparison. We present a theoretical analysis of the quality of estimation to guarantee the reliability of Odd Sketch-based estimators. Our experiments confirm this efficiency, and demonstrate the efficiency of Odd Sketches in comparison with $b$-bit minwise hashing schemes on association rule learning and web duplicate detection tasks.
OriginalsprogEngelsk
TitelProceedings of the 23rd international conference on World wide web : WWW '14
Antal sider10
ForlagAssociation for Computing Machinery
Publikationsdato2014
Sider109-118
ISBN (Elektronisk)978-1-4503-2744-2
DOI
StatusUdgivet - 2014

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  • Best Paper Award

    Ninh Dang Pham (Deltager)

    7 apr. 201411 apr. 2014

    Aktivitet: Andre aktivitetstyperAndet (priser, ekstern undervisning samt andet). - Priser, stipendier, udnævnelser

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