Article

Evaluating similarity measures: a large-scale study in the orkut social network

Authors:

Ellen Spertus,

Mehran Sahami,

Orkut BuyukkoktenAuthors Info & Claims

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Pages 678 - 684

https://doi.org/10.1145/1081870.1081956

Published: 21 August 2005 Publication History

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Abstract

Online information services have grown too large for users to navigate without the help of automated tools such as collaborative filtering, which makes recommendations to users based on their collective past behavior. While many similarity measures have been proposed and individually evaluated, they have not been evaluated relative to each other in a large real-world environment. We present an extensive empirical comparison of six distinct measures of similarity for recommending online communities to members of the Orkut social network. We determine the usefulness of the different recommendations by actually measuring users' propensity to visit and join recommended communities. We also examine how the ordering of recommendations influenced user selection, as well as interesting social issues that arise in recommending communities within a real social network.

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Yang CChen LWang HShang SMao RZhang X(2023)Dynamic Set Similarity Join: An Update Log Based ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.312663135:4(3727-3741)Online publication date: 1-Apr-2023
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Evaluating similarity measures: a large-scale study in the orkut social network

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Information

Published In

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

August 2005

844 pages

ISBN:159593135X

DOI:10.1145/1081870

General Chair:
Robert Grossman
University of Illinois at Chicago & Open Data Partners, USA
,
Program Chairs:
Roberto Bayardo
IBM Almaden Research, USA
,
Kristin Bennett
RPI, USA

Permission to make digital or hard copies of all or part 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, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2005

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KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2005

Illinois, Chicago, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.3390/e25121635
Yang CChen LWang HShang SMao RZhang X(2023)Dynamic Set Similarity Join: An Update Log Based ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.312663135:4(3727-3741)Online publication date: 1-Apr-2023
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Uhartegaray RD'Orazio LDamigos MKalogeros E(2023)Scalable Computation of Fuzzy Joins Over Large Collections of JSON Data2023 IEEE International Conference on Fuzzy Systems (FUZZ)10.1109/FUZZ52849.2023.10309759(01-06)Online publication date: 13-Aug-2023
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