Density Ratio Based Personalised Ranking from Implicit Feedback

This repository is an example pytorch implementation of DREGN-CF (arXiv):

Density Ratio Based Personalised Ranking from Implicit Feedback Riku Togashi, Masahiro Kato, Mayu Otani, Shin'ichi Satoh.
In Proceedings of the Web Conference 2021 (WWW '21).

DREGN-CF is a method based on semi-supervised density ratio estimation. It minimises a risk derived from the weighted Bregman divergence. The default implementation of DREGN-CF uses the risk estimator without importance sampling. The codes are mainly based on the pytorch implementation of LightGCN by authors (here).

Files

• data/
  • gowalla/
    • need train.txt and test.txt here;
  • yelp2018/
    • need train.txt and test.txt here;
  • amazonbook/
    • need train.txt and test.txt here;
• conf/:
  • dataset: "dataset_name.yaml" contains the path configurations;
  • model: "model_name.yaml" contains the model configurations;
  • config.yaml: default config file;
• code/:
  • main.py: ;
  • train.py: the implementation of training process;
  • model.py: the implementation of DREGN-CF;
  • dataset.py: the implementation of the mini-batch sampler;
  • logger.py: logging utilities;
  • evaluator/: the code of C++ evaluator.

Set up directories

Need to prepare directories to save logs, codes (including confs), and models. The configuration for the directory paths is in "config.yaml".

  $ mkdir -p runs/log runs/code_backup runs/model_backup

Set up for C++ evaluator

This repository includes the efficient C++ evaluator implemented in the original repository of LightGCN.

  $ python setup.py build_ext --inplace

Running the code

• AmazonBook

  $ python code/main.py experiment_name=amazon_book_dregncf model=dregncf dataset=amazon_book optimize.batch_size=2500 optimize.lr=0.01 optimize.train_epochs=100 reg_weight=5e-2 dr_upper_bound=80
  

• Yelp2018

  $ python code/main.py experiment_name=yelp2018_dregncf model=dregncf dataset=yelp2018 optimize.batch_size=2500 optimize.lr=0.01 optimize.train_epochs=150 reg_weight=5e-2 dr_upper_bound=70
  

• Gowalla

  $ python code/main.py experiment_name=gowalla_dregncf model=dregncf dataset=gowalla optimize.batch_size=2500 optimize.lr=0.01 optimize.train_epochs=60 reg_weight=6e-2  dr_upper_bound=70