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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

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IBP
 
 
M3F
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README

To run iPM3F (M3F and iBPM3F likewise):
1. set working dir in MATLAB to iPM3F

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

3. a little bit configuration [OPTIONAL]
  >> % defaults to 'weak' & 'no validation'
  >> weakopts = []; % for the 'weak' setting
  >> validid = 1; % use the 1st validation set

4. compile mex-files and initialize
  >> init;

5. set range for regularization constant tuning [OPTIONAL]
  >> % defaults to the entire range
  >> rid_s = 1; rid_t = numel(regvals); % tune all the candidate values

6. turn on parallel computing and logging [OPTIONAL]
  >> matlabpool open <your_profile>;
  >> diary(fullfile(savedir, 'log.txt'));

7. train the model and test performance
  >> train_ipm3f;

8. 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

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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

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