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» Probabilistic models for discovering e-communities
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IIR
2010
13 years 10 months ago
An Empirical Comparison of Collaborative Filtering Approaches on Netflix Data
Recommender systems are widely used in E-Commerce for making automatic suggestions of new items that could meet the interest of a given user. Collaborative Filtering approaches co...
Nicola Barbieri, Massimo Guarascio, Ettore Ritacco
SAC
2009
ACM
14 years 3 months ago
Applying latent dirichlet allocation to group discovery in large graphs
This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (suc...
Keith Henderson, Tina Eliassi-Rad
RECOMB
2009
Springer
14 years 9 months ago
Spatial Clustering of Multivariate Genomic and Epigenomic Information
The combination of fully sequence genomes and new technologies for high density arrays and ultra-rapid sequencing enables the mapping of generegulatory and epigenetics marks on a g...
Rami Jaschek, Amos Tanay
RECOMB
2006
Springer
14 years 9 months ago
Detecting MicroRNA Targets by Linking Sequence, MicroRNA and Gene Expression Data
MicroRNAs (miRNAs) have recently been discovered as an important class of non-coding RNA genes that play a major role in regulating gene expression, providing a means to control th...
Jim C. Huang, Quaid Morris, Brendan J. Frey
CIA
2007
Springer
14 years 2 months ago
Learning Initial Trust Among Interacting Agents
Trust learning is a crucial aspect of information exchange, negotiation, and any other kind of social interaction among autonomous agents in open systems. But most current probabil...
Achim Rettinger, Matthias Nickles, Volker Tresp