Overview
CDTF (Coordinate Descent for Tensor Factorization) and
SALS (Subset Alternating Least Square) are

tensor factorization algorithms for a highorder and largescale tensor

parallelizable in distributed environments

scalable with the order and size of data; the number of parameters; and the number of machines

require several orders of magnitude less memory space than their competitors
CDTF has an advantage in terms of memory usage and flexibility, while
SALS has an advantage in terms of convergence speed
Papers
CDTF and
SALS are described in the following papers:

Distributed Methods for Highdimensional and Largescale Tensor Factorization.
Kijung Shin and U Kang.
IEEE International Conference on Data Mining (ICDM) 2014, Shenzhen, China
[PDF] [BIBTEX]

Fully Scalable Methods for Distributed Tensor Factorization.
Kijung Shin, Lee Sael, and U Kang
IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 29, no. 1, pp. 100113, Jan 2017
[PDF] [Supplementary Document] [BIBTEX]
Codes
The source codes used in the papers are available.
[ver.1 (ICDM)]
[ver.2 (TKDE)]
They include:

CDTF Hadoop version & single machine (multithread) version

SALS Hadoop version & single machine (multithread) version