Field-Aware Factorization Machine Implemented by Java, with An Experiment Using Criteo Dataset.
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Get The DataSet
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refer to: https://github.com/guestwalk/kaggle-2014-criteo/blob/master/README, 'Get The Dataset' part.


Run main_script.sh
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feature_engineer --> split train/validation set --> subsample --> change to libffm format.
then run libffm:
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java -Xmx65g -jar ffm.jar <eta> <lambda> <iter> <factor> <norm> <rand> <trset> <vaset>
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eta: used for learning rate
lambda: used for L2 regularization
iter: max iterations
factor: latent factor num
norm: instance wise normalization
rand: use random instance order when training
trset: train set
vaset: validation set


Experiment Results:
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norm and rand only affect training speed.
best eta is about 0.1, bigger eta hurt validation logloss, smaller eta get slow convergence.
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when eta=0.1, factor=4, iter=10:
lambda            0.00000  0.00001  0.00010  0.00100  0.01000  0.10000
best_logloss      0.45061  0.44930  0.44951  0.46919  0.58700  0.69321
convergence iter     3        5        10       10      10    10(very slow)
convergence iter 10 means not oberserve convergence.
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when lambda=0.0001, eta=0.1, iter=30:
k=4,8,12 doesn't affect best_logloss and convergence iter.