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Algorithms for Bayesian Poisson factorization
License: GNU General Public License v3.0
This project forked from premgopalan/hgaprec
Algorithms for Bayesian Poisson factorization
License: GNU General Public License v3.0
Reference --------- @article{DBLP:journals/corr/GopalanHB13, author = {Prem Gopalan and Jake M. Hofman and David M. Blei}, title = {Scalable Recommendation with Poisson Factorization}, journal = {CoRR}, volume = {abs/1311.1704}, year = {2013}, ee = {http://arxiv.org/abs/1311.1704}, bibsource = {DBLP, http://dblp.uni-trier.de} } Installation ------------ Required libraries: gsl, gslblas, pthread On Linux/Unix run ./configure make; make install On Mac OS, the location of the required gsl, gslblas and pthread libraries may need to be specified: ./configure LDFLAGS="-L/opt/local/lib" CPPFLAGS="-I/opt/local/include" make; make install The binary 'gaprec' will be installed in /usr/local/bin unless a different prefix is provided to configure. (See INSTALL.) HGAPREC: Hierarchical Gamma Poisson factorization based recommendation tool ---------------------------------------------------------------------------- **hgaprec** [OPTIONS] -dir <string> path to dataset directory with 3 files: train.tsv, test.tsv, validation.tsv (for examples, see example/movielens-1m) -m <int> number of items -n <int> number of users -k <int> number of factors -rfreq <int> assess convergence and compute other stats <int> number of iterations default: 10 -a -b set hyperparameters -c default: a = b = c = d = 0.3 -d -hier learn the hierarchical model with Gamma priors on user and item scale parameters -bias use user and item bias terms -binary-data treat observed data as binary (if rating > 0 then rating is treated as 1) -gen-ranking generate ranking file to use in precision computation; see example -msr write out ranking file assuming the test file is based on leave-one-out, i.e., leaving one item out for each user Example -------- 1. Input data To run inference you need 4 files: {train,test,validation,test_users}.tsv in tab-separated format. See the movielens files in example/ (Additional files are generated from these basic files during evaluation. More on this later.) 2. Running the command You can run "hgaprec" directly or using the scripts/run.pl Perl script. Since hgaprec has numerous options, the perl script is recommended. To setup the Perl script, do the following: a. Set the "$dataloc" variable to the location of the data set directory. For example, for the movielens data, $dataloc = "/n/fs/example/movielens"; b. Set the location of the installed hgaprec binary. For example, $gapbin = "/n/fs/bin/hgaprec"; c. Run the script in one of the following ways: (fit the hierarchical model, i.e., HPF) <path-to>/scripts/run.pl -dataset movielens -hier (fit HPF to data treated as binary) <path-to>/scripts/run.pl -dataset movielens -hier -binary (fit the non-hierarchical model, i.e., BPF to data and treat data as binary) <path-to>/scripts/run.pl -dataset movielens -binary (fit the non-hierarchical model with user, item bias terms, i.e., BPF+bias to data) <path-to>/scripts/run.pl -dataset movielens -bias (fit the non-hierarchical model with user, item bias terms, i.e., BPF+bias to data treated as binary) <path-to>/scripts/run.pl -dataset movielens -bias -binary d. Additional options to the Perl script -K <integer>: set the latent dimensions K -label <string>: set a label for the output directory -seed <integer>: set the pseudo-random number generator seed -logl: compute ELBO every X iterations (expensive!)
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