more info: http://sharnett.github.com/netflix/

TO RUN C++ VERSION:
make a new directory called "cpp" in the netflix data folder (need ~1.5 GB space)
edit the file "data_folder.txt" to point to the netflix data folder
make all
./create_binaries
./probe_bs
./funk -i

uses alglib (source already included) http://www.alglib.net/

logs crap to a sqlite3 database, this is optional

on Linux, needs BLAS

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a ton of files, what are they?

* alglib
    Code for L-BFGS and non-linear conjugate gradient

* best_avgs
* train_and_dump_avgs
    Solves least squares problem to find an offset for each movie and user. The
    base prediction for movie i and user j is then 
        \p_ij = \mu + \mu_i + \mu_j
    Where \mu is the overall average rating, \mu_i is the offset for movie i,
    and \mu_j is the offset for user j. It cross-validates against the probe
    set to find the best choices that prevent over-fitting. This is explained
    a bit more in the "Winners' paper" link.

    best_avgs finds the optimal regularization parameter. The other code solves
    with this parameter and write the result to disk.

* best_lambda
    Finds best regularization parameter for a given number of features.

* create_binaries
    Reads in the Netflix Prize data from the text files and converts it to more
    convenient binary format for fast loading.

* find_average
    Finds overall average rating, sum(ratings)/len(ratings)

* fmin
    Some freely available code for minimizing a 1D function over an interval 
    without using derivatives. It's the same algorithm as MATLAB's fminbnd. This
    gets used when tuning meta-parameters during cross-validation.

* funk
    Main program

* load
    Helper functions to read various data from disk.

* optimizers
    Code for L-BFGS, CG, SGD, GD

* predictor
    Class that predicts ratings. Maintains a user and movie matrix, the product
    of which is the predictions matrix.

* probe_bs
    Removes ratings in the probe set from the data to get a pure training set.
    The probe set is then used for cross-validation.