Item-based collaborative filtering recommendation algorithms
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Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Evaluating collaborative filtering recommender systems
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Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Being accurate is not enough: how accuracy metrics have hurt recommender systems
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Investigating interactions of trust and interest similarity
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Restricted Boltzmann machines for collaborative filtering
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A model of a trust-based recommendation system on a social network
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ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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A survey of collaborative filtering techniques
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Use of social network information to enhance collaborative filtering performance
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A matrix factorization technique with trust propagation for recommendation in social networks
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Multi-value probabilistic matrix factorization for IP-TV recommendations
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Adaptive social similarities for recommender systems
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New objective functions for social collaborative filtering
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Circle-based recommendation in online social networks
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On top-k recommendation using social networks
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Bayesian-Inference-Based Recommendation in Online Social Networks
IEEE Transactions on Parallel and Distributed Systems
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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.