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EqBal-RS: Mitigating popularity bias in recommender systems

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Abstract

Recommender systems are deployed heavily by many online platforms for better user engagement and providing recommendations. Despite being so popular, several works have shown the existence of popularity bias due to the non-random nature of missing data. Popularity bias leads to the recommendation of only a few popular items causing starvation of many non-popular items. This paper considers an easy-to-understand metric to evaluate the popularity bias as the difference between mean squared error on popular and non-popular items. Then, we propose EqBal-RS, a novel re-weighting technique that updates the weights of popular and non-popular items. Re-weighting ensures that both item sets are equally balanced during training using a trade-off function between overall loss and popularity bias. Our experiments on real-world datasets show that EqBal-RS outperforms the existing state-of-art algorithms in terms of accuracy, quality, and fairness. EqBal-RS works well on the proposed and existing popularity bias metrics and has significantly reduced runtime. The code is publicly available at https://github.com/eqbalrs/EqBalRS

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Availability of supporting data and material

All datasets used in the experiments are publicly available and cited.

Code Availability

The code has been made publicly available at https://github.com/eqbalrs/EqBalRS

Notes

  1. https://grouplens.org/datasets/movielens

  2. https://webscope.sandbox.yahoo.com

  3. https://nijianmo.github.io/amazon

  4. https://optuna.org

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Acknowledgements

We express our gratitude to the reviewers for their invaluable feedback, which has greatly enhanced the manuscript’s quality. We also wish to express our heartfelt thanks to the entire editorial and production team for their efforts.

Funding

The authors thank the Prime Minister Research Fellowship for generously funding Shivam Gupta (ID: 2901481) for this work. The research is further supported by the Department of Science & Technology, India, with grant number SRG/2020/001138.

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  1. Indian Institute of Technology Ropar, Rupnagar, Punjab, India

    Shivam Gupta, Kirandeep Kaur & Shweta Jain

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Contributions

Shivam Gupta formulated the problem statement, proposed algorithm EqBal-RS and executed experiments. Kirandeep Kaur helped review the literature, providing useful insights and executing MF, MFR baselines. Dr. Shweta Jain provided valuable insights and feedback throughout the work. All authors wrote the manuscript text and reviewed the manuscript.

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Correspondence to Shivam Gupta.

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Gupta, S., Kaur, K. & Jain, S. EqBal-RS: Mitigating popularity bias in recommender systems. J Intell Inf Syst (2023). https://doi.org/10.1007/s10844-023-00817-w

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