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hgaprec's Introduction

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!)

hgaprec's People

Contributors

lcharlin avatar premgopalan avatar

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hgaprec's Issues

What is the range of the prediction score?

I ran the code and got a file itemrank.txt. Most of the third column of numbers, which denoted the prediction ranking, were 0.xxx; some of them were 1.xxx and 2.xxx; just little of them were 3.xxx and 4.xxx. Is it normal in your consumption? Should the prediction score range 1 to 5?

Setting of $dataloc

I am trying to run the code by setting up the Perl code. As the comments in the run.pl said:

set the "prefix" path for the data sets
e.g., if data set resides in /scratch/pgopalan/kdd3/example/movielens
set as follows
my $dataloc = "/scratch/pgopalan/hgaprec/analysis/data";

(1)I am confused by this one. What should I set the address of the dataloc if I, say, put the data on
"/Users/yobichi/Downloads/hgaprec-master/example/movielens"? By the way, should I set the dataloc variable in run.pl?

(2) The link: http://jmh-recs.cloudapp.net/home/pgopalan/KDD/output, where the readme.txt of output data can not be opened. Where can I see the format of input and out data to get a clearer understanding?

Clarification on input and output formats

Hello there,

I am really excited to test the software. I work on mutational processes in cancer and the model has some of the properties I need.

I wonder if you could help by clarifying the input and output formats. I managed to install and run the example, but don't fully understand the formats. Would you help by pointing me to a description of the input and all the files that the program outputs? I am mostly interested in exploring the components, a bit like you did with the movie preferences in the article, and trying to understand common themes.

Kind regards
Dominik

Installing on Mac OS X

How do you install hgaprec on Mac OS X?

(I'm opening this as a separate issue in case it helps somebody. Answer below.)

Fail to run other dataset

I store the yelp data(train.tsv, test.tsv, validation.tsv, test_user.tsv) at /Users/yobichi/Downloads/code_RecSys13/ so I set the
$dataloc = "/Users/yobichi/Downloads/code_RecSys13" at run.pl
and then I run "/Users/yobichi/Downloads/hgaprec-master/scripts/run.pl -dataset yelp_data -hier " in command.
There was an tricky error: cannot open file /Users/yobichi/Downloads/hgaprec-master/example/netflix.

What should I do? Thank you very much.

README *and* README.md?

should there really be both?

wiggins@tantanmen{hgaprec}48: diff README README.md
17c17

< The binary 'hgaprec' will be installed in /usr/local/bin unless a

The binary 'gaprec' will be installed in /usr/local/bin unless a
20,21c20,21
< HGAPREC: Hierarchical Gamma Poisson factorization based recommendation tool

< ----------------------------------------------------------------------------

GAPREC: Gamma Poisson factorization based recommendation tool

23c23

< hgaprec [OPTIONS]

gaprec [OPTIONS]
62c62

< (1) ../src/hgaprec -dir ../example/movielens -n 6040 -m 3900 -k 100 -rfreq 10

(1) ../src/gaprec -dir ../example/movielens -n 6040 -m 3900 -k 100 -rfreq 10
88,93c88,90
< ../src/hgaprec -dir ../example/movielens -n 6040 -m 3900 -k 100 -rfreq 10 -hier
< ../src/hgaprec -dir ../example/movielens -n 6040 -m 3900 -k 100 -rfreq 10 -bias
< ../src/hgaprec -dir ../example/movielens -n 6040 -m 3900 -k 100 -rfreq 10 -binary-data
< ../src/hgaprec -dir ../example/movielens -n 6040 -m 3900 -k 100 -rfreq 10 -binary-data -bias
< ../src/hgaprec -dir ../example/movielens -n 6040 -m 3900 -k 100 -rfreq 10 -hier -bias -binary-data

< ... and so on

../src/gaprec -dir ../example/movielens -n 6040 -m 3900 -k 100 -rfreq 10 -bias
../src/gaprec -dir ../example/movielens -n 6040 -m 3900 -k 100 -rfreq 10 -binary-data
../src/gaprec -dir ../example/movielens -n 6040 -m 3900 -k 100 -rfreq 10 -binary-data -bias
wiggins@tantanmen{hgaprec}49:

Problem with installation

I tried to install this on both ubuntu and mac os computers, but I always get this error when executing "make":

Making all in src
make[1]: Entering directory /home/dlmocdm/hgaprec-master2/src' source='ratings.cc' object='ratings.o' libtool=no \ DEPDIR=.deps depmode=none /bin/bash ../depcomp \ g++ -DPACKAGE_NAME=\"hgaprec\" -DPACKAGE_TARNAME=\"hgaprec\" -DPACKAGE_VERSION=\"0.1\" -DPACKAGE_STRING=\"hgaprec\ 0.1\" -DPACKAGE_BUGREPORT=\"[email protected]\" -DPACKAGE=\"hgaprec\" -DVERSION=\"0.1\" -DHAVE_LIBGSLCBLAS=1 -DHAVE_LIBPTHREAD=1 -DHAVE_LIBGSL=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STDINT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_SYS_FILE_H=1 -DHAVE_SYS_TIME_H=1 -DHAVE_UNISTD_H=1 -DHAVE__BOOL=1 -DHAVE_STDBOOL_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MALLOC=1 -DHAVE_GETTIMEOFDAY=1 -DHAVE_LOCALTIME_R=1 -DHAVE_MEMSET=1 -DHAVE_MKDIR=1 -DHAVE_STRERROR=1 -DHAVE_STRTOL=1 -I. -I/opt/local/include -O3 -c -o ratings.o ratings.cc /bin/bash: ../depcomp: No such file or directory make[1]: *** [ratings.o] Error 127 make[1]: Leaving directory/home/dlmocdm/hgaprec-master2/src'
make: *** [all-recursive] Error 1

Any idea how can I solve this?

Thanks!

depcomp missing

depcomp is a soft link to a file that might not exist on user's system.

Posterior predictive check

Hi, in the paper, you compute a simulated ratings vector for each user by spreading a preference budget b across k components. Is that code available here? Having a beta[i][k] matrix would be super useful too if I wanted to use it for other discrepancy functions! Thanks so much!

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