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ICML
2004
IEEE
14 years 8 months ago
Unifying collaborative and content-based filtering
Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a nove...
Justin Basilico, Thomas Hofmann
BMCBI
2007
147views more  BMCBI 2007»
13 years 7 months ago
Hon-yaku: a biology-driven Bayesian methodology for identifying translation initiation sites in prokaryotes
Background: Computational prediction methods are currently used to identify genes in prokaryote genomes. However, identification of the correct translation initiation sites remain...
Yuko Makita, Michiel J. L. de Hoon, Antoine Danchi...
BMCBI
2008
175views more  BMCBI 2008»
13 years 7 months ago
Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces
Background: Protein-protein interactions are ubiquitous and essential for all cellular processes. High-resolution X-ray crystallographic structures of protein complexes can reveal...
Andrew J. Bordner, Andrey A. Gorin
SIGIR
2004
ACM
14 years 29 days ago
A joint framework for collaborative and content filtering
This paper proposes a novel, unified, and systematic approach to combine collaborative and content-based filtering for ranking and user preference prediction. The framework inco...
Justin Basilico, Thomas Hofmann
ICML
2007
IEEE
14 years 8 months ago
Gradient boosting for kernelized output spaces
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
Florence d'Alché-Buc, Louis Wehenkel, Pierr...