Paper Info
Title
LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems.
Abstract
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 sharedmemory 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.
Year
Venue
Keywords
2016
JOURNAL OF MACHINE LEARNING RESEARCH
Matrix factorization,non-negative matrix factorization,binary matrix factorization,logistic matrix factorization,one-class matrix factorization,stochastic gradient method,adaptive learning rate,parallel computation
Field
DocType
Volume
Computer science,Artificial intelligence,Incomplete LU factorization,Quadratic sieve,Distributed computing,Recommender system,Shared memory,Incomplete Cholesky factorization,Matrix decomposition,Parallel computing,Non-negative matrix factorization,Big data,Machine learning
Journal
17
ISSN
Citations 
PageRank 
1532-4435
1
0.35
References 
Authors
0
6
Name
Order
Citations
PageRank
Wei-Sheng Chin12368.76
Bo-Wen Yuan241.73
Meng-Yuan Yang310.35
Yong Zhuang425413.88
Yu-Chin Juan52529.54
Chih-Jen Lin6202861475.84