Dynamic Poisson factorization (dPF)

This repository provides the dynnormprec (Dynamic Normal Poisson factorization) recommendation tool.

Reference

Dynamic Poisson factorization by Laurent Charlin, Rajesh Ranganath, James McInerney and David Blei, Rec'Sys 2015.

Acknowledgements

This code is built from a fork of Prem Gopalan's Poisson factorization code (https://github.com/premgopalan/hgaprec).

Installation

Required libraries: gsl, gslblas, pthread, openMP

On Linux/Unix run

./configure;make

On Mac OS (untested), the location of the required gsl, gslblas, pthread, and openMP libraries may need to be specified:

./configure LDFLAGS="-L/opt/local/lib" CPPFLAGS="-I/opt/local/include"; make

The binary 'dynnormprec' will be installed in /usr/local/bin unless a different prefix is provided to configure. (See INSTALL.)

Running

./dynnormprec -dir data_dir -n num_users -m num_items -k num_factors [-rfreq] [-vprior] [-binary-data]

Required arguments

• -dir path to dataset directory with 3 files: train.tsv, test.tsv, validation.tsv (for examples, see data/)
• -n number of users
• -m number of items
• -k number of factors

Optional arguments

• -rfreq assess convergence and compute other stats every number of iterations [default: 10]
• -vprior set variance hyperparameters [default 10]
• -non-binary-data treat observed data as non-binary (warning the code is untested w/o this option) [default false]
• -num_threads set the number of parallel threads to run [default 1]
• -tpl length of each time period (measured in seconds) [default 6*30 days ~ 6 months]

Example

1. Input data. To run inference you need 4 files: {train,test,validation,test_users}.tsv in tab-separated format. See the lastFM files in dat/. To use this dataset you will first need to uncompress the data files (e.g., for train.tsv.gz gunzip train.tsv.gz and similarly with validation.tsv.gz and test.tsv.gz)

2. Running the command. We recommend running dynnormprec using the following options:

./src/dynnormprec -n 992 -m 983 -dir dat/ -k 5 -rfreq 10 -vprior 10 -num_threads TH;

• TH is the number of available CPUs on the machine.
• Changing the number of factors and the variance hyperparameters will have an effect on the quality of the resulting model.