LIBMF++ is a library for large-scale sparse matrix factorization. For the optimization problem it solves, please refer to [1]. This library is based on LIBMF.

Table of Contents

• Installation
• Data Format
• Command Line Usage
• Examples
• References

Installation

• Unix & Cygwin

  Type make' to build mf-train' and `mf-precict.'

Data Format

The data format is:

<row_idx> <col_idx> <value>

Note: If the values in the test set are unknown, please write dummy zeros.

Command Line Usage

• `mf-train'

  usage: mf-train [options] training_set_file [model_file]

  options: -l : set regularization parameter (default 0.1) -k : set number of latent features (default 8) -t : set number of iterations (default 20) -s : set number of threads (default 12) -r : set rho parameter(default 0.1) -e : set epsilon parameter(default 0.001) -p : set path to the validation set -v : set number of folds for cross validation --quiet: quiet mode (no outputs)

  In the training process, the following information is printed on the screen:

  - iter: the index of iteration
  - time: time cost of iteration 
  - tr_rmse: RMSE in the training set
  - va_rmse: RMSE in the validation set if `-p' is specified
  - obj: objective function value
  

  Here tr_rmse' and obj' are estimation because calculating true values can be time-consuming. In the end of training process the true tr_rmse is printed.

• `mf-predict'

  usage: mf-predict test_file model_file output_file

Examples

mf-train bigdata.tr.txt model

train a model using the default parameters

mf-train -l 0.5 -k 16 -t 30 -r 0.05 -e 0.00001 -s 4 bigdata.tr.txt model

train a model using the following parameters:

regularization cost = 0.5
latent factors = 16
iterations = 30
rho = 0.05
epsilon = 0.00001
threads = 4

mf-train -p bigdata.te.txt bigdata.tr.txt model

use bigdata.te.txt as validation set

mf-train -v 5 bigdata.tr.txt

do five fold cross validation

mf-train --quiet bigdata.tr.txt

do not print message to screen

mf-predict bigdata.te.txt model output

do prediction

References

[1] Wei F, Guo H, Cheng S, et al. AALRSMF: An Adaptive Learning Rate Schedule for Matrix Factorization[C]//Asia-Pacific Web Conference. Springer International Publishing, 2016: 410-413.