Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users

This is our implementation and experimental data for the paper:

Roger Zhe Li, Julián Urbano, Alan Hanjalic (2021). Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users. In Proceedings of WSDM '21, Virtual Event, Israel, March 8-12, 2021.

Please cite our WSDM '21 paper if you use our code and data. Thanks!

Author: Roger Zhe Li (https://www.zhe-li.me)

Environment Settings

We use PyTorch 1.6.0 as the main deep learning framework for implementation. The data analysis relies heavily on pyGAM.

Example to run the code.

The instruction of commands has been clearly stated in the code (see the parse_args function).

Run NAECF and DeepCoNN:

python3 test.py --dataset instant_video --coef_u 0.5 --coef_i 0.5 --seed 1992 --batch_size 256 --mode 4

Run MF:

python3 test_mf.py --dataset instant_video --seed 1992 --batch_size 256 --mode 5

Dataset

We provide three processed datasets: Amazon Instant Video, Amazon Digital Music and BeerAdvocate. The BeerAdvocate dataset keeps users with at least 5 interactions, and sample 25% users in the original dataset.

Cite

Please cite our WSDM'21 paper if you use the code.

@inproceedings{li2021leave,
  title={Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users},
  author={Li, Roger Zhe and Urbano, Juli{\'a}n and Hanjalic, Alan},
  booktitle={Proceedings of the 14th ACM International Conference on Web Search and Data Mining},
  pages={103--111},
  year={2021}
}

License

• The paper is licensed under a Creative Commons Attribution International 4.0 License.
• Databases and their contents are distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.
• Software is distributed under the terms of the MIT License.

Last Update Date: January 21, 2021