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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Bibliographic Details
Published in:Computer communications Vol. 41; pp. 1 - 10
Main Authors: Yang, Xiwang, Guo, Yang, Liu, Yong, Steck, Harald
Format: Journal Article
Language:English
Published: Kidlington Elsevier B.V 15.03.2014
Elsevier
Subjects:
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
Online Access:Get full text
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Summary: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.
ISSN:0140-3664
1873-703X
DOI:10.1016/j.comcom.2013.06.009