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!)