Make sure that you successfully compiled the source code, or have executables in the directory.
First, you can run the program without specifying anything. In this case, the program runs all algorithms with a default demo data file "netflix_3m1k", bundled with the install file. For more advanced use, you can specify three kinds of arguments. More than one argument can be used simultaneously, independent to each other. For an argument which was not specified, the default value in the table below would be applied. Detailed explanation about data split can be found in Documentation section.

Options	Syntax	Default value	Example
Input file	-f [File Name]	netflix_3m1k	java Prea -f movieLens_1M
Data Split	-s simple [Test ratio]	simple, 0.2	java Prea -s simple 0.2
	-s pred [Split file name]		java Prea -s pred netflix_3m1k_split.txt
	-s kcv [Fold count]		java Prea -s kcv 5
	-s rank [Train point per user]*		java Prea -s rank 20
Algorithm	-a [Algorithm code. See below.]	(Run all.)	java Prea -a regsvd [Parameters of the algorithm. See below.]

* This number contains the number of data points used for validation set.

1. Download source code file, and unzip it in a directory.
2. Locate the Matlab current directory to the directory. In GUI version, you can use the following red box. Make sure that there are all executable files and two matlab files (matlab2arff.m, run.m).
3. Load the data file in Matlab. The matrix should in the format of users (row) by items (column). If it is reversed, it is necessary to transpose it at this point.
4. Run the wrapper function with the following formats.
   1. The simplest format specifies only matrix name in Matlab. In this case, it runs constant model (Median) as a default. For example,
      
      prea(movielens);
      
   2. You can specify the output ARFF file from the Matlab matrix, if you want to use it again later. For example, the following code will create "movielens.arff" file in the directory.
      
      prea(movielens, 'movielens');
      
   3. With another parameter, you can specify a CF algorithm keyword to be used. For example,
      prea(movielens, 'movielens', 'itembased');
      prea(movielens, 'movielens', 'bpmf');
      prea(movielens, 'movielens', 'npca');
      
   4. Lastly, you can specify arguments for each algorithm. This takes two steps; first, you should make "options" with parameters you want to specify, then, add it as a parameter to "run" function. (Without specifying these parameters just as the previous case, default parameters are used.)
      options = struct('feature_count', 5, 'maxiter', 20);
      prea(movielens, 'movielens', 'bpmf');
      
      The list of available options for each algorithm is as follows:

      Algorithm	Keyword	Parameter List
      Constant	const	None
      Overall Average	allavg	None
      User Average	useravg	None
      Item Average	itemavg	None
      Random	random	None
      User-based CF	userbased	neighbor_size
      Item-based CF	itembased	neighbor_size
      Slope One	slopeone	None
      Regularized SVD	regsvd	feature_count, learning_rate, regularizer, maxiter
      NMF	nmf	feature_count, learning_rate, maxiter
      PMF	pmf	feature_count, learning_rate, regularizer, momentum, maxiter
      Bayesian PMF	bpmf	feature_count, maxiter
      Nonlinear PMF	nlpmf	feature_count, learning_rate, momentum, maxiter, kernel_inverse_width, kernel_var_rbf, kernel_var_bias, kernel_var_white
      Fast NPCA	npca	validation_ratio, maxiter
      Rank-based CF	rank	kernel_width
      Singleton Global LLORMA	sgllorma	feature_count, learning_rate, regularizer, maxiter, maxmodel
      Singleton Parallel LLORMA	spllorma	feature_count, learning_rate, regularizer, maxiter, maxmodel, thread

5. The output from the toolkit is shown in Matlab screen.