Information filtering on coupled social networks.

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preferen...

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Autores principales:	Da-Cheng Nie, Zi-Ke Zhang, Jun-Lin Zhou, Yan Fu, Kui Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f7cdb2668fbc40b7bc76d3b59017e407
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spelling	oai:doaj.org-article:f7cdb2668fbc40b7bc76d3b59017e4072021-11-25T06:09:22ZInformation filtering on coupled social networks.1932-620310.1371/journal.pone.0101675https://doaj.org/article/f7cdb2668fbc40b7bc76d3b59017e4072014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25003525/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks.Da-Cheng NieZi-Ke ZhangJun-Lin ZhouYan FuKui ZhangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e101675 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Da-Cheng Nie Zi-Ke Zhang Jun-Lin Zhou Yan Fu Kui Zhang Information filtering on coupled social networks.
description	In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks.
format	article
author	Da-Cheng Nie Zi-Ke Zhang Jun-Lin Zhou Yan Fu Kui Zhang
author_facet	Da-Cheng Nie Zi-Ke Zhang Jun-Lin Zhou Yan Fu Kui Zhang
author_sort	Da-Cheng Nie
title	Information filtering on coupled social networks.
title_short	Information filtering on coupled social networks.
title_full	Information filtering on coupled social networks.
title_fullStr	Information filtering on coupled social networks.
title_full_unstemmed	Information filtering on coupled social networks.
title_sort	information filtering on coupled social networks.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/f7cdb2668fbc40b7bc76d3b59017e407
work_keys_str_mv	AT dachengnie informationfilteringoncoupledsocialnetworks AT zikezhang informationfilteringoncoupledsocialnetworks AT junlinzhou informationfilteringoncoupledsocialnetworks AT yanfu informationfilteringoncoupledsocialnetworks AT kuizhang informationfilteringoncoupledsocialnetworks
_version_	1718414139987066880

Information filtering on coupled social networks.

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