An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 10th international conference on Intelligent user interfaces
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast nonparametric matrix factorization for large-scale collaborative filtering
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Trust based recommender system for the semantic web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning to recommend with trust and distrust relationships
Proceedings of the third ACM conference on Recommender systems
Social Recommendation with Interpersonal Influence
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Learning to recommend with explicit and implicit social relations
ACM Transactions on Intelligent Systems and Technology (TIST)
Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation
Proceedings of the fifth ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Social recommendation problems have drawn a lot of attention recently due to the prevalence of social networking sites. The experiments in previous literature suggest that social information is very effective in improving traditional recommendation algorithms. However, explicit social information is not always available in most of the recommender systems, which limits the impact of social recommendation techniques. In this paper, we study the following two research problems: (1) In some systems without explicit social information, can we still improve recommender systems using implicit social information? (2) In the systems with explicit social information, can the performance of using implicit social information outperform that of using explicit social information? In order to answer these two questions, we conduct comprehensive experimental analysis on three recommendation datasets. The result indicates that: (1) Implicit user and item social information, including similar and dissimilar relationships, can be employed to improve traditional recommendation methods. (2) When comparing implicit social information with explicit social information, the performance of using implicit information is slightly worse. This study provides additional insights to social recommendation techniques, and also greatly widens the utility and spreads the impact of previous and upcoming social recommendation approaches.