https://sites.google.com/cornell.edu/recsys2021tutorial

yuta-saito

Last update: Nov 10, 2022

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Deep Learning recsys2021-tutorial

Overview

Counterfactual Learning and Evaluation for Recommender Systems (RecSys'21 Tutorial)

Materials for "Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances", a tutorial delivered at the 15th ACM Conference on Recommender System (RecSys'21).

Presenters: Yuta Saito (Cornell University, USA) and Thorsten Joachims (Cornell University, USA).
Tutorial website: https://sites.google.com/cornell.edu/recsys2021tutorial
Recording: https://youtu.be/HMo9fQMVB4w
Tutorial proposal: https://dl.acm.org/doi/10.1145/3460231.3473320
List of tutorials at RecSys2021: https://recsys.acm.org/recsys21/tutorials/
Reference Package (Open Bandit Pipeline): https://github.com/st-tech/zr-obp
Survey of related papers: https://github.com/hanjuku-kaso/awesome-offline-rl

examples: brief examples describing how to use Open Bandit Pipeline with synthetic data, classification data, and real-world bandit data
simulations: simulation codes comparing a wide variety of existing OPE estimators on synthetic data
real-world: a brief demo of OPE/OPL on real bandit dataset (need Open Bandit Dataset)

The Google Colab version of implementations (examples) are available here.

Requirements and Setup

The Python environment is built using poetry. You can build the same environment as in our examples and simulations by cloning the repository and running poetry install directly under the folder (if you have not install poetry yet, please run pip install poetry first.).

# clone the obp repository
git clone [email protected]:usaito/recsys2021-tutorial.git
cd benchmark/ope

# build the environment with poetry
poetry install

# activate jupyter-lab environment
poetry run jupyter lab

The versions of Python and used packages are as follows.

[tool.poetry.dependencies]
python = "^3.9,<3.10"
scikit-learn = "0.24.2"
numpy = "^1.21.2"
pandas = "^1.3.3"
obp = "0.5.1"
matplotlib = "^3.4.3"
jupyterlab = "^3.1.13"

Contact

If you have any question, please feel free to contact: [email protected]

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Comments

Are the tutorial videos still available?

I'm trying to revisit this tutorial and recall having watched the videos for them. However, I can't seem to find them anymore and was wondering if the tutorial videos are still available online?

opened by eugeneyan 4
hyperparameter tuning on real data
Hello Yuta,

Thank you for your terrific work on this tutorial. I have several questions on the hyperparameter tuning, and hope you could help me.

I guess the parameter tau in SyntheticBanditDataset should be beta now.

I think the synthetic data (including the pscore) is generated by using linear_behavior_policy. If we don't know the behavior policy except the logged bandit feedback, could we still be able to train the IPWLearner?

I think the calc_ground_truth_policy_value function is still estimating the policy value of $\hat{\pi}_b$, am I right?

Thank you very much.

Best, Chunpai
opened by Chunpai 2

https://sites.google.com/cornell.edu/recsys2021tutorial

Related tags

Overview

Counterfactual Learning and Evaluation for Recommender Systems (RecSys'21 Tutorial)

Contents

Requirements and Setup

Contact

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Comments

Are the tutorial videos still available?

hyperparameter tuning on real data

Owner

yuta-saito

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