A survey of collaborative filtering based social recommender systems

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to her. Recent studies demonstrate that information from social networks can be exploited to improve accuracy of recommendations. In this paper,...

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Vydáno v:	Computer communications Ročník 41; s. 1 - 10
Hlavní autoři:	Yang, Xiwang, Guo, Yang, Liu, Yong, Steck, Harald
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier B.V 15.03.2014 Elsevier
Témata:	Applied sciences Collaborative filtering Computer science; control theory; systems Computer systems and distributed systems. User interface Exact sciences and technology Recommender system Social network Software Collaborative filtering Social network Recommender system Matrix factorization Recommendation
ISSN:	0140-3664, 1873-703X
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Abstract	Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to her. Recent studies demonstrate that information from social networks can be exploited to improve accuracy of recommendations. In this paper, we present a survey of collaborative filtering (CF) based social recommender systems. We provide a brief overview over the task of recommender systems and traditional approaches that do not use social network information. We then present how social network information can be adopted by recommender systems as additional input for improved accuracy. We classify CF-based social recommender systems into two categories: matrix factorization based social recommendation approaches and neighborhood based social recommendation approaches. For each category, we survey and compare several representative algorithms.
AbstractList	Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to her. Recent studies demonstrate that information from social networks can be exploited to improve accuracy of recommendations. In this paper, we present a survey of collaborative filtering (CF) based social recommender systems. We provide a brief overview over the task of recommender systems and traditional approaches that do not use social network information. We then present how social network information can be adopted by recommender systems as additional input for improved accuracy. We classify CF-based social recommender systems into two categories: matrix factorization based social recommendation approaches and neighborhood based social recommendation approaches. For each category, we survey and compare several representative algorithms.
Author	Steck, Harald Guo, Yang Liu, Yong Yang, Xiwang
Author_xml	– sequence: 1 givenname: Xiwang surname: Yang fullname: Yang, Xiwang email: xy271@nyu.edu organization: Polytechnic Institute of NYU, Brooklyn, NY, USA – sequence: 2 givenname: Yang surname: Guo fullname: Guo, Yang email: Yang.Guo@alcatel-lucent.com organization: Bell Labs, Alcatel-Lucent, Holmdel, NJ, USA – sequence: 3 givenname: Yong surname: Liu fullname: Liu, Yong email: yongliu@poly.edu organization: Polytechnic Institute of NYU, Brooklyn, NY, USA – sequence: 4 givenname: Harald surname: Steck fullname: Steck, Harald email: hsteck@netflix.com organization: Netflix Inc., Los Gatos, CA, USA
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Snippet	Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to...
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SubjectTerms	Applied sciences Collaborative filtering Computer science; control theory; systems Computer systems and distributed systems. User interface Exact sciences and technology Recommender system Social network Software
Title	A survey of collaborative filtering based social recommender systems
URI	https://dx.doi.org/10.1016/j.comcom.2013.06.009
Volume	41
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