Deep Causal Reasoning for Recommender Systems

The codes are associated with the following paper:

Deep Causal Reasoning for Recommendations,
Yaochen Zhu, Jing Yi, Jiayi Xie and Zhenzhong Chen,
ArXiv Preprints 2022. [pdf]

Environment

The codes are written in Python 3.6.5.

• numpy == 1.16.3
• pandas == 0.21.0
• tensorflow-gpu == 1.15.0
• tensorflow-probability == 0.8.0

Dataset Acquirement and Simulation

• Acquire the movielens-1m and amazon-vg datasets:
  The original datasets can be found [here] and [here].
  Preprocess the data with data_sim/raw/prepare_data.py.

• Preprocess the original dataset: cd to data_sim/raw folder, run
  python prepare_data.py --dataset Name --simulate {exposure, ratings}.

• Fit the exposure and rating distribution via VAEs: cd to data_sim folder, run
  python train.py --dataset Name --simulate {exposure, ratings}.

• Simulate the causal dataset under various confounding levels: python simulate.py --dataset Name --simulate {exposure, ratings}.

• The simulated datasets are in casl/data folder

Fitting the Exposure and Rating Models

• Split the simulated causal datasets into train/val/test:
  cd to casl_rec/data folder, run
  python preprocess.py --dataset Name --split 5.

• Train the exposure model, conduct predictive check:
  python train_exposure.py --dataset Name --split [0-4]

• Infer the subsititute confounders:
  python infer_subs_conf.py --dataset Name --split [0-4]

• Train the potential rating prediction model:
  python train_ratings.py --dataset Name --split [0-4]

• Predict the scores for hold-out users:
  python evaluate_model.py --dataset Name --split [0-4]

For advanced argument usage, run the code with --help argument.