Publication
Non-negative matrix factorization (NMF) has become a standard tool in data mining, information retrieval, and signal processing. It is used to factorize a non-negative data matrix ...
SIGIR
2011
ACM
12 years 7 months ago
2011
ACM
A key challenge in recommender system research is how to effectively profile new users, a problem generally known as cold-start recommendation. Recently the idea of progressivel...
ICASSP
2011
IEEE
12 years 8 months ago
2011
IEEE
Non-negative Matrix Factorization (NMF) has found use in single channel separation of audio signals, as it gives a parts-based decomposition of audio spectrograms where the parts ...
BMCBI
2010
13 years 1 months ago
2010
Background: External stimulations of cells by hormones, cytokines or growth factors activate signal transduction pathways that subsequently induce a re-arrangement of cellular gen...
ICDM
2009
IEEE
13 years 2 months ago
2009
IEEE
Matrix factorization (MF) models have proved efficient and well scalable for collaborative filtering (CF) problems. Many researchers also present the probabilistic interpretation o...
RECSYS
2010
ACM
13 years 2 months ago
2010
ACM
A ranking approach, ListRank-MF, is proposed for collaborative filtering that combines a list-wise learning-to-rank algorithm with matrix factorization (MF). A ranked list of item...
ICML
2010
IEEE
13 years 2 months ago
2010
IEEE
Matrix factorization into the product of lowrank matrices induces non-identifiability, i.e., the mapping between the target matrix and factorized matrices is not one-to-one. In th...
ICDM
2010
IEEE
13 years 2 months ago
2010
IEEE
Thousands of scientific conferences happen every year, and each involves a laborious scientific peer review process conducted by one or more busy scientists serving as Technical/Sc...
JMLR
2010
13 years 2 months ago
2010
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statisti...
CIKM
2010
Springer
13 years 2 months ago
2010
Springer
Matrix factorization methods are among the most common techniques for detecting latent components in data. Popular examples include the Singular Value Decomposition or Nonnegative...