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.

References

[1]

Breese, J.; Heckerman, D.; Kadie, C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (Madison, Wisconsin, 1998). Morgan Kaufmann.

Digital Library

Google Scholar

[2]

Cover, T.M., and Thomas, J.A. Elements of Information Theory. Wiley, New York, 1991.

Digital Library

Google Scholar

[3]

Deshpande, M., and Karypis, G. Item-Based Top-N Recommendation Algorithms. ACM Transactions on Information Systems 22(1) (January 2004), 143--177.

Digital Library

Google Scholar

[4]

Domingos, P. Prospects and Challenges for Multi-Relational Data Mining. ACM SIGKDD Exploration Newsletter 5(1) (July 2003).

Digital Library

Google Scholar

[5]

Dumais, S.; Joachims, T.; Bharat, K.; Weigend, A. SIGIR 2003 Workshop Report: Implicit Measures of User Interests and Preferences. SIGIR Forum 37(2) (Fall 2003).

Digital Library

Google Scholar

[6]

Harman, D. Ranking Algorithms. In W. B. Frakes and R. Baeza-Yates (ed.), Information Retrieval: Data Structures & Algorithms (chapter 14). Upper Saddle River, NJ, USA: Prentice Hall, 1992.

Digital Library

Google Scholar

[7]

Joachims, T. Evaluating Retrieval Performance Using Clickthrough Data. In Proceedings of the SIGIR Workshop on Mathematical/Formal Methods in Information Retrieval (2002). ACM Press, New York, NY.

Google Scholar

[8]

Kautz, H.; Selman, Bart; Shah, M. Referral Web: Combining Social Networks and Collaborative Filtering. Communications of the ACM 45(8) (March 1997).

Digital Library

Google Scholar

[9]

Kitts, B.; Freed, D.; Vrieze, M. Cross-Sell: A Fast Promotion-Tunable Customer-Item Recommendation Method based on Conditionally Independent Probabilities. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Boston, 2000). ACM Press, New York, NY, 437--446.

Digital Library

Google Scholar

[10]

Lehmann, E.L. Testing Statistical Hypotheses (second edition). Springer-Verlag, 1986.

Google Scholar

[11]

Raghavan, P. Social Networks and the Web (Invited Talk). In Advances in Web Intelligence: Proceedings of the Second International Atlantic Web Intelligence Conference, May 2004. Springer-Verlag, Heidelberg.

Crossref

Google Scholar

[12]

Salton, G. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison Wesley, Reading, MA, 1989.

Digital Library

Google Scholar

[13]

Sarwar, B.; Karypis, G.; Konstan, J.; Reidl, J. Item-Based Collaborative Filtering Recommendation Algorithms. In Proceedings of the Tenth International Conference on the World Wide Web (WWW10) (Hong Kong, 2001). ACM Press, New York, NY, 285--295.

Digital Library

Google Scholar

[14]

Spertus, Ellen. Too Much Information. Orkut Media Selections, January 19, 2005. Available online at "http://media.orkut.com/articles/0078.html".

Google Scholar

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

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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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Published: 21 August 2005

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August 21 - 24, 2005

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McFadden J(2023)Carving Nature at Its Joints: A Comparison of CEMI Field Theory with Integrated Information Theory and Global Workspace TheoryEntropy10.3390/e2512163525:12(1635)Online publication date: 8-Dec-2023
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Similarity measures for Collaborative Filtering-based Recommender Systems: Review and experimental comparison

A Similarity Measure for Collaborative Filtering with Implicit Feedback

An improved item-based collaborative filtering using a modified Bhattacharyya coefficient and user–user similarity as weight

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