Social Poisson Factorization
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conf
dat/filmtrust
scripts
src
LICENSE.txt
README.md

README.md

Social Poisson Factorization (SPF)

(C) Copyright 2014-2016, Allison J.B. Chaney

This software is distributed under the MIT license. See LICENSE.txt for details.

Repository Contents

  • conf contains a base configure file for running LibRec to do model comparisons
  • scripts bash and python scripts for data processing and running experiments
  • src C++ source code
  • dat example data
  • README.md this file

Data

The input format for data is tab-separted files with integer values:

user id    item id    rating

The ratings should be separated into training, testing, and validation data; scripts/process_data.py helps divide data into these different sets. This script also culls the user network such that only connections that have at least one item in common are included.

python process_data.py [ratings-file] [network-file] [output-dir]

Alternatively, data with time information (like shown below) can be processed with process_time_data.py which takes the same arguments as process_data.py. This will split the data according to time; ratings are implicit and therefore binary.

user id    item id    unix time

Running SPF

  1. Clone the repo: git clone https://github.com/ajbc/spf.git
  2. Navigate to the spf/src directory
  3. Compile with make
  4. Run the executable, e.g.: ./spf --data ~/my-data/ --out my-fit

SPF Options

Option Arguments Help Default
help print help information
verbose print extra information while running off
out dir save directory, required
data dir data directory, required
svi use stochastic VI (instead of batch VI) off for < 10M ratings in training
batch use batch VI (instead of SVI) on for < 10M ratings in training
a_theta a shape hyperparamter to theta (user preferences) 0.3
b_theta b rate hyperparamter to theta (user preferences) 0.3
a_beta a shape hyperparamter to beta (item attributes) 0.3
b_beta b rate hyperparamter to beta (item attributes) 0.3
a_tau a shape hyperparamter to tau (user influence) 2
b_tau b rate hyperparamter to tau (user influence) 5
a_delta a shape hyperparamter to delta (item bias) 0.3
b_delta b rate hyperparamter to delta (item bias) 0.3
social-only only consider social aspect of factorization (SF) include factors
factor-only only consider general factors (no social; PF) include social
bias include a bias term for each item no bias
binary assume ratings are binary integer
directed assume network is directed undirected
seed seed the random seed time
save_freq f the saving frequency. Negative value means no savings for intermediate results. 20
eval_freq f the intermediate evaluating frequency. Negative means no evaluation for intermediate results. -1
conv_freq f the convergence check frequency 10
max_iter max the max number of iterations 300
min_iter min the min number of iterations 30
converge c the change in rating log likelihood required for convergence 1e-6
final_pass do a final pass on all users and items no final pass
sample sample_size the stochastic sample size 1000
svi_delay tau SVI delay >= 0 to down-weight early samples 1024
svi_forget kappa SVI forgetting rate (0.5,1] default 0.75
K K the number of general factors 100

Running an Experiment

  1. Download and compile code for comparison models: cd scripts/; ./setup.sh; cd ..
  2. Kick off fits for multiple models with the script (from scripts directory):
./study [data-dir] [output-dir] [K] [directed/undirected]