Deep deconfounded recommender (Deep-Deconf) for paper "Deep causal reasoning for recommendations"

Overview

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.

Owner
Yaochen Zhu
Master student at WHU.
Yaochen Zhu
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