Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback

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About

This repository accompanies the real-world experiment conducted in the paper "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback" by Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata, which has been accepted to WSDM'20.

If you find this code useful in your research then please cite:

@inproceedings{saito2020unbiased,
  title={Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback},
  author={Saito, Yuta and Yaginuma, Suguru and Nishino, Yuta and Sakata, Hayato and Nakata, Kazuhide},
  booktitle={Proceedings of the 13th International Conference on Web Search and Data Mining},
  pages={501--509},
  year={2020}
}

Dependencies

• python==3.7.3
• numpy==1.16.2
• pandas==0.24.2
• scikit-learn==0.20.3
• tensorflow==1.15.0
• plotly==3.10.0
• mlflow==1.4.0
• pyyaml==5.1

Running the code

To run the simulation with real-world data, download the Yahoo! R3 dataset and put train.txt and test.txt files into data/ directory. Then, navigate to the src/ directory and run the command

for model in wmf expomf crmf
do
  python main.py $model --preprocess_data &
done

This will run real-world experiments conducted in Section 6. After running the experimens, you can visualize the results by running the following command in the src/ directory

python visualize.py

You can check the experimental parameters actually used in our experiments in the config.yaml file.

Once the code is finished executing, you can find the results of ranking metrics of all methods in ./logs/overall/ directory. In addition, the figures are stored in ./plots/results/ directory.

Figures

By running the codes above, you can obtain the figures below.

	Figure 2: DCG	Figure 2: Recall	Figure 2: MAP
For all items
For rare items