Chin, Wei-Sheng; Yuan, Bo-Wen; Yang, Meng-Yuan; Zhuang, Yong; Juan, Yu-Chin; Lin, Chih-Jen LIBMF: a library for parallel matrix factorization in shared-memory systems. (English) Zbl 1360.65119 J. Mach. Learn. Res. 17, Paper No. 86, 5 p. (2016). Summary: Matrix factorization (MF) plays a key role in many applications such as recommender systems and computer vision, but MF may take long running time for handling large matrices commonly seen in the big data era. Many parallel techniques have been proposed to reduce the running time, but few parallel MF packages are available. Therefore, we present an open source library, LIBMF, based on recent advances of parallel MF for shared-memory systems. LIBMF includes easy-to-use command-line tools, interfaces to C/C++ languages, and comprehensive documentation. Our experiments demonstrate that LIBMF outperforms state of the art packages. LIBMF is BSD-licensed, so users can freely use, modify, and redistribute the code. Cited in 1 Document MSC: 65F30 Other matrix algorithms (MSC2010) 15A23 Factorization of matrices 65Y05 Parallel numerical computation 65Y10 Numerical algorithms for specific classes of architectures 65Y15 Packaged methods for numerical algorithms Keywords:matrix factorization; non-negative matrix factorization; binary matrix factorization; logistic matrix factorization; one-class matrix factorization; stochastic gradient method; adaptive learning rate; parallel computation Software:LIBMF PDFBibTeX XMLCite \textit{W.-S. Chin} et al., J. Mach. Learn. Res. 17, Paper No. 86, 5 p. (2016; Zbl 1360.65119) Full Text: Link