Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
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
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Expert Systems with Applications: An International Journal
Exploring generative models of tripartite graphs for recommendation in social media
Proceedings of the 4th International Workshop on Modeling Social Media
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Robust multivariate autoregression for anomaly detection in dynamic product ratings
Proceedings of the 23rd international conference on World wide web
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The rapidly increasing availability of online social networks and the well-known effect of social influence have motivated research on social-network based recommenders. Social influence and selection together lead to the formation of communities of like-minded and well connected users. Exploiting the clustering of users and items is one of the most important approaches for model-based recommendation. Users may belong to multiple communities or groups, but only a few clustering algorithms allow clusters to overlap. One of these algorithms is the probabilistic EM clustering method, which assumes that data is generated from a mixture of Gaussian models. The mixed membership stochastic block model (MMB) transfers the idea of EM clustering from conventional, non-relational data to social network data. In this paper, we introduce a generalized stochastic blockmodel (GSBM) that models not only the social relations but also the rating behavior. This model learns the mixed group membership assignments for both users and items in an SRN. GSBM can predict the future behavior of users, both the rating of items and creation of links to other users. We performed experiments on two real life datasets from Epinions.com and Flixster.com, demonstrating the accuracy of the proposed GSBM for rating prediction as well as link prediction.