1. Basic information
2. Overview
3. Requirements
4. How to use
   1. Input and output
   2. How to run
   3. How to give parameters
5. Demo example

• Authors: Haekyu Park, Jinhong Jung, and U Kang
• Program name: MFRWR
• Version: 1.0
• Last updated: 28 Aug 2017
• Main contact: Haekyu Park ([email protected])

This package is a set of implementaions of recommender systems based on matrix factorization and random walk with restart. These methods are compared in each recommendation scenarios in the following paper: A comparative study of matrix factorization and random walk with restart in recommender system. We suggest 4 matrix factorization methods and 4 random walk with restart methods for the following cases:

• When explicit feedback ratings are given
• When implicit feedback ratings are given
• When bias terms are introduced
• When side information is used

• python 3.*
• numpy
• pandas
• scipy
• We recommend you to use Anaconda.

1. Input and output

• Input
  • Ratings and side information are able to be given as input.
  • All the input should be given as tab-separated files.
    • Rating file should have three columns for user_id, item_id, and rating.
    • User_id should not overlapped with item_id.
    • Side information file should have two columns for user/item_id and value.
  • The name of rating files should be 'rating.tsv'.
• Output
  • We print out Spearman's rho, precision@k, and recall@k with stdout.
  • You may write log generator for the results for yourself.
• Intermediate outputs
  • We generate intermediate outputs such as vectors and biases of users and items.
  • All the intermediate outputs have their file names under the rule: 'method name_dataset name_parameters_fold.txt'.

2. How to run

• First go to ./code/.
• You can run the code by typing python main.py.
• You can optionally give parameters with two approaches.
  • 1. By appending '--argument_type argument_value'. For example, if you want to run matrix factorization with explicit ratings, and you want to set learning rate = 0.05, lambda = 0.3, and dimension = 5, you can run the code as follows: python main.py --method MF_exp --lr 0.05 --lamb 0.3 --dim 5
  • 2. By using config You can make a configuration file that contains all the parameters you give. Then you can run the code by giving path of the config file as follows: python main.py --config True --config_path ./myconfig.conf Rules to make config files are as follows.
    • All parameters should be separated with '\n'.
    • Argument type and its values for each parameters should be separated with ';'. For example, a config file to run matrix factorization is as follows.

     method;MF_exp
     dataset;filmtrust
     lr;0.05
     lamb;0.3
     dim;5
    

The parameters you can give are as follows.

• (*1)

  • Name of methods can be one of the followings.
  • split5folds, MF_exp, MF_imp, MF_bias, MF_side, RWR_exp, RWR_imp, RWR_bias, RWR_side.
  • When 'split5folds' is given, you can split rating data into 5 folds. The split should be done before running MF/RWR methods.

• (*2)

  • You can give file paths of side information with list.
  • This should be given by config file.
  • For example, if you want to give '../data/movielens/age.tsv' and '../data/movielens/gender.tsv' for side information, you can give --side_paths argument in the config file as follows.

   side_paths;['../data/movielens/age.tsv', '../data/movielens/gender.tsv']
  

• (*3)

  • You should declare types of entities in side information.
  • Each type can be one of the followings: 'u', 'i', and 's'.
  • 'u' indicates users, 'i' indicates items, and 's' indicates similarity attributes.
  • This should be given by config file.
  • For example, if you have '../data/movielens/age.tsv' and '../data/movielens/gender.tsv' which have first column for user id and second column for user attribute, a config file can be written as follows.

   side_paths;['../data/movielens/age.tsv', '../data/movielens/gender.tsv']
   entity_types;[['u', 's'], ['u', 's']].
  

• (*4)

  • You can sample seed users for RWR methods.
  • Sampling options are given because the methods take too much time if many users are included.

You can run MF_exp with filmtrust dataset. Please run demo.sh by typing ./demo.sh.