Optimization-Based Approaches for Maximizing Aggregate Recommendation Diversity

Gediminas Adomavicius
Gediminas Adomavicius
[email protected]
Department of Information Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455
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,
YoungOk Kwon
YoungOk Kwon
[email protected]
Division of Business Administration, Sookmyung Women's University, Seoul, 140-742 Korea
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Department of Information Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455

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YoungOk Kwon

[email protected]

Division of Business Administration, Sookmyung Women's University, Seoul, 140-742 Korea

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Published Online:24 Feb 2014https://doi.org/10.1287/ijoc.2013.0570

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ijoc.2013.0570-sm-ec.pdf (95 KB)

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cover image INFORMS Journal on Computing

Volume 26, Issue 2

Spring 2014

Pages 199-413

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Received:June 01, 2011
Accepted:June 01, 2013
Published Online:February 24, 2014

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Gediminas Adomavicius, YoungOk Kwon (2014) Optimization-Based Approaches for Maximizing Aggregate Recommendation Diversity. INFORMS Journal on Computing 26(2):351-369.

https://doi.org/10.1287/ijoc.2013.0570

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Optimization-Based Approaches for Maximizing Aggregate Recommendation Diversity

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