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Abstract
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.
Original language | English |
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Title of host publication | Proceedings of the 23rd international conference on World wide web : WWW '14 |
Number of pages | 10 |
Publisher | Association for Computing Machinery |
Publication date | 2014 |
Pages | 109-118 |
ISBN (Electronic) | 978-1-4503-2744-2 |
DOIs | |
Publication status | Published - 2014 |
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Dive into the research topics of 'Efficient estimation for high similarities using odd sketches'. Together they form a unique fingerprint.Activities
- 1 Other (prizes, external teaching and other activities) - Prizes, scholarships, distinctions
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Best Paper Award
Ninh Dang Pham (Participant)
7 Apr 2014 → 11 Apr 2014Activity: Other activity types › Other (prizes, external teaching and other activities) - Prizes, scholarships, distinctions
Press/Media
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University student invents algorithm speeds up internet searches
Ninh Dang Pham
27/11/2014
1 item of Media coverage
Press/Media: Press / Media
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Opfindelse fra Danmark gør computere hurtigere
Ninh Dang Pham
26/11/2014
1 item of Media coverage
Press/Media: Press / Media