This paper examines the scalable parallel implementation of QR factorization of a general matrix, targeting SMP and multi-core architectures. Two implementations of algorithms-by-...
Data sparsity, scalability and prediction quality have been recognized as the three most crucial challenges that every collaborative filtering algorithm or recommender system conf...
The scalable parallel implementation, targeting SMP and/or multicore architectures, of dense linear algebra libraries is analyzed. Using the LU factorization as a case study, it is...
In recent years, matrix approximation for missing value prediction has emerged as an important problem in a variety of domains such as recommendation systems, e-commerce and onlin...
Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn...
Steffen Rendle, Christoph Freudenthaler, Lars Schm...