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IPM3F

Source code and data for the paper "Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction" accepted to NIPS'2012 and "Fast Max-Margin Matrix Factorization with Data Augmentation" accepted to ICML'2013

Install / Use

npx skills add chokkyvista/iPM3F

Installs into whichever agent you are using.

About this skill

Quality Score

0/100

Supported Platforms

Universal

README

To run iPM3F (M3F and iBPM3F likewise):

  1. set working dir in MATLAB to iPM3F

  2. load data

load('../data/MovieLens-1M.mat');

  1. a little bit configuration [OPTIONAL]

% defaults to 'weak' & 'no validation' weakopts = []; % for the 'weak' setting validid = 1; % use the 1st validation set

  1. compile mex-files and initialize

init;

  1. set range for regularization constant tuning [OPTIONAL]

% defaults to the entire range rid_s = 1; rid_t = numel(regvals); % tune all the candidate values

  1. turn on parallel computing and logging [OPTIONAL]

matlabpool open <your_profile>; diary(fullfile(savedir, 'log.txt'));

  1. train the model and test performance

train_ipm3f;

  1. turn off parallel computing and logging [OPTIONAL]

diary off; matlabpool close;

Put together:

cd iPM3F; load('../data/MovieLens-1M.mat'); init; train_ipm3f;

To run GiPM3F or GM3F, you may need to download daSVM (https://github.com/chokkyvista/daSVM) as well and put it at the same level as the iPM3F root directory:

  • daSVM
  • iPM3F |- GM3F |- GiPM3F |- IBP |- common |- data

Related Skills

View on GitHub
GitHub Stars9
CategoryDevelopment
Updated1y ago
Forks10

Languages

C++

Security Score

55/100

Audited on Feb 18, 2025

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