Recently, there are increasing reports that most datasets can be actually stored in disks of a single off-the-shelf workstation, and utilizing out-of-core methods is much cheaper and even faster than using a distributed system. For these reasons, out-of-core methods have been actively developed for machine learning and graph processing. The goal of this paper is to develop an efficient out-of-core matrix completion method based on coordinate descent approach. Coordinate descent-based matrix completion (CD-MC) has two strong benefits over other approaches: 1) it does not involve heavy computation such as matrix inversion and 2) it does not have step-size hyper-parameters, which reduces the effort for hyper-parameter tuning. Existing solutions for CD-MC have been developed and analyzed for in-memory setting and they do not take disk-I/O into account. Thus, we propose OCAM, a novel out-of-core coordinate descent algorithm for matrix completion. Our evaluation results and cost analyses provide sound evidences supporting the following benefits of OCAM: (1) Scalability – OCAM is a truly scalable out-of-core method and thus decomposes a matrix larger than the size of memory, (2) Efficiency – OCAM is super fast. OCAM is up to 10x faster than the state-of-the-art out-of-core method, and up to 4.1x faster than a competing distributed method when using eight machines.

• OCAM: Out-of-core Coordinate Descent Algorithm for Matrix Completion (Information Sciences, 2019)
  • Dongha Lee, Jinoh Oh, Hwanjo Yu

• OpenMP library

• Parameters for build-gcsc

• Parameters for train-ocam

• This code is implemented based on the author code of Yu et al., "Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems", in ICDM, 2012.
• We recommend you to use a solid-state disk (SSD) for efficient and scalable matrix factorization.