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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 http://books.nips.cc/papers/files/nip…
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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