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messing with netflix prize data
Home Page: http://sharnett.github.com/netflix/
This project forked from sharnett/netflix
messing with netflix prize data
Home Page: http://sharnett.github.com/netflix/
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 -------------------------------------------------------------------------------- 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.
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