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Human behavior / Collaborative filtering / Collective intelligence / Information retrieval / Recommender system / Social networking service / Online dating service / Reciprocity / Social information processing / Collaboration / Information science


User recommendation in reciprocal and bipartite social networks --a case study of online dating Kang Zhao+1, Xi Wang+, Mo Yu*, and Bo Gao# + Department of Management Sciences, The University of Iowa, USA.
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Document Date: 2013-11-11 20:15:39


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File Size: 1,58 MB

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City

Iowa City / /

Company

IC / LinkedIn / Netflix / Facebook / K (d) RC / RC / /

Country

United States / China / /

Currency

GBP / /

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Event

Product Recall / Product Issues / /

Facility

The University of Iowa / Penn State University / Network Center / College of Information Sciences / Beijing Jiaotong University / /

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IndustryTerm

data mining techniques / authorship network / business transaction networks / heterosexual online dating / online experience / job application networks / heterosexual dating network / recommender systems / text mining technique / reciprocal social network / bipartite network / user-movie network / reciprocal and bipartite social networks / online dating websites / reciprocal networks / product purchase network / online dating website / bipartite dating network / social networking / heterosexual dating networks / un-directed and un-weighted dating network / online dating / bipartite networks / people search / bipartite reciprocal social networks / social networks / Internet users / social network / typical bipartite social network / bipartite social networks / online dating network / /

Organization

Department of Management Sciences / Penn State University / Network Center / The University of Iowa / College of Information Sciences and Technology / Beijing Jiaotong University / /

Person

Kang Zhao / Mo Yu / /

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Position

Researcher / author / /

Product

RC / M-16 / /

ProvinceOrState

Oregon / Iowa / /

Technology

data mining / /

SocialTag