A local search approximation algorithm for k-means clustering
Proceedings of the eighteenth annual symposium on Computational geometry
Proceedings of the 10th international conference on Intelligent user interfaces
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
Aggregating inconsistent information: Ranking and clustering
Journal of the ACM (JACM)
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
Towards trust inference from bipartite social networks
DBSocial '12 Proceedings of the 2nd ACM SIGMOD Workshop on Databases and Social Networks
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The World Wide Web has transformed into an environment where users both produce and consume information. In order to judge the validity of information, it is important to know how trustworthy its creator is. Since no individual can have direct knowledge of more than a small fraction of information authors, methods for inferring trust are needed. We propose a new trust inference scheme based on the idea that a trust network can be viewed as a random graph, and a chain of trust as a path in that graph. In addition to having an intuitive interpretation, our algorithm has several advantages, noteworthy among which is the creation of an inferred trust-metric space where the shorter the distance between two people, the higher their trust. Metric spaces have rigorous algorithms for clustering, visualization, and related problems, any of which is directly applicable to our results.