This is the official pytorch implementation of AutoDebias, an automatic debiasing method for recommendation.

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Deep Learning AutoDebias
Overview

AutoDebias

This is the official pytorch implementation of AutoDebias, a debiasing method for recommendation system. AutoDebias is proposed in the paper:

AutoDebias: Learning to Debias for Recommendation

by Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin and Keping Yang

Published at SIGIR 2021.

Introduction

AutoDebias is an automatic debiasing method for recommendation system based on meta learning, exploiting a small amout of uniform data to learn de-biasing parameters and using these parameters to guide the learning of the recommendation model.

Environment Requirement

The code runs well under python 3.8.5. The required packages are as follows:

  • pytorch == 1.4.0
  • numpy == 1.19.1
  • scipy == 1.5.2
  • pandas == 1.1.3
  • cppimport == 20.8.4.2

Datasets

We use two public datasets (Yahoo!R3 and Coat) and a synthetic dataset (Simulation).

  • user.txt: biased data collected by normal policy of recommendation platform. For Yahoo!R3 and Coat, each line is user ID, item ID, rating of the user to the item. For Simulation, each line is user ID, item ID, position of the item, binary rating of the user to the item.
  • random.txt: unbiased data collected by stochastic policy where items are assigned to users randomly. Each line in the file is user ID, item ID, rating of the user to the item.

Run the Code

Explicit feedback

  • For dataset Yahoo!R3:
python train_explicit.py --dataset yahooR3
  • For dataset Coat:
python train_explicit.py --dataset coat

Implicit feedback

  • For dataset Yahoo!R3:
python train_implicit.py --dataset yahooR3
  • For dataset Coat:
python train_implicit.py --dataset coat

Feedback on list recommendation

  • For dataset Simulation:
python train_list.py --dataset simulation

Contact

Please contact [email protected] or [email protected] if you have any questions about the code and paper.

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Comments
  • Update ex.cpp

    Update ex.cpp

    According to the official description of cppimport, this line is essential: "the comment at top is essential to opt in to cppimport. Don't forget this! "

    If you do not add it, it will raise this error:

    "ModuleNotFoundError: No module named 'utils.ex'"

    opened by chongminggao 0
  • ModuleNotFoundError: No module named 'utils.ex'

    ModuleNotFoundError: No module named 'utils.ex'

    Hi. Thank you for sharing the code.

    I download your code and directly run it. But it shows: ModuleNotFoundError: No module named 'utils.ex'

    If I delete import utils.ex as ex and add exp= cppimport.imp_from_filepath('utils/ex.cpp'), it still shows the following error: #include <forward_list> ^~~~~~~~~~~~~~ 1 error generated. error: command 'gcc' failed with exit status 1

    Can you help me how to run your code if possible. Thank you very much!

    opened by DongXiang-CV 0
  • High Performance of Biased Methods

    High Performance of Biased Methods

    Hello, I am very happy to see the release of Autodebias.

    When I seek to reproduce the MF_biased and MF_combine approaches, they exhibits much better performance than the IPS/DR/CausE approaches. The MF_combine approach, in particular, reaches an AUC of 0.735-0.737, which is competitive to AutoDebias.

    I'm very curious as to why the IPS/DR/CausE approach would fail in this implementation, and why the biased/combine approaches would perform better than debiased approaches. This issue is important to me because this strange result makes me question the validity of the fundamental causal approaches (IPS/DR) in practice.

    Thanks!

    opened by XPBooster 1
Owner
Dong Hande
Dong Hande
GitHub
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