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» Learning and Inference in Massive Social Networks
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CIKM
2009
Springer
14 years 4 months ago
A social recommendation framework based on multi-scale continuous conditional random fields
This paper addresses the issue of social recommendation based on collaborative filtering (CF) algorithms. Social recommendation emphasizes utilizing various attributes informatio...
Xin Xin, Irwin King, Hongbo Deng, Michael R. Lyu
JCB
2006
185views more  JCB 2006»
13 years 9 months ago
A Probabilistic Methodology for Integrating Knowledge and Experiments on Biological Networks
Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed ...
Irit Gat-Viks, Amos Tanay, Daniela Raijman, Ron Sh...
CHI
2011
ACM
13 years 1 months ago
Apolo: making sense of large network data by combining rich user interaction and machine learning
Extracting useful knowledge from large network datasets has become a fundamental challenge in many domains, from scientific literature to social networks and the web. We introduc...
Duen Horng Chau, Aniket Kittur, Jason I. Hong, Chr...
WWW
2010
ACM
14 years 4 months ago
Sampling community structure
We propose a novel method, based on concepts from expander graphs, to sample communities in networks. We show that our sampling method, unlike previous techniques, produces subgra...
Arun S. Maiya, Tanya Y. Berger-Wolf
SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
12 years 3 days 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...