Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Robust collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
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
Operators for propagating trust and their evaluation in social networks
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Collaborative filtering recommender systems
The adaptive web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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In this article, we propose an evolution of trust-based recommender systems that only relies on local information and can be deployed on top of existing social networks. Our approach takes into account friends' similarity and confidence on ratings, but limits data exchange to direct friends, in order to prevent ratings from being globally known. Therefore, calculations are limited to locally processed algorithms, privacy concerns can be taken into account and algorithms are suitable for decentralized or peer-to-peer architectures. We have implemented and evaluated our approach against five others, using the Epinions trust network. We show that local information with good default scoring strategies are sufficient to cover more users than classical collaborative filtering and trust-based recommender systems. Regarding accuracy, our approach performs better than most others, specially for cold start users, despite using less information.