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» Predicting Interests of People on Online Social Networks
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NIPS
2008
13 years 8 months ago
Non-stationary dynamic Bayesian networks
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Joshua W. Robinson, Alexander J. Hartemink
ECR
2010
129views more  ECR 2010»
13 years 7 months ago
The design of a reliable reputation system
Next generation Web 2.0 communities and distributed P2P systems rely on the cooperation of diverse user populations spread across numerous administrative and security domains. Zero...
Gayatri Swamynathan, Kevin C. Almeroth, Ben Y. Zha...
SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
11 years 9 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...
CORR
2011
Springer
217views Education» more  CORR 2011»
12 years 11 months ago
Coarse-Grained Topology Estimation via Graph Sampling
Many online networks are measured and studied via sampling techniques, which typically collect a relatively small fraction of nodes and their associated edges. Past work in this a...
Maciej Kurant, Minas Gjoka, Yan Wang, Zack W. Almq...
SIGCOMM
2010
ACM
13 years 7 months ago
Trust no one: a decentralized matching service for privacy in location based services
We propose a new approach to ensure privacy in location based services, without requiring any support from a"trusted" entity. We observe that users of location based ser...
Sharad Jaiswal, Animesh Nandi