mlsanityData to 7 Oct 2026

RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems

arXiv 2201.04399 ↗First posted 12 Jan 2022cs.IR cs.AI cs.LGcited by 10

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Abstract (arXiv)

Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data. Due to the pervasiveness of recommender systems in online technologies, researchers have carried out several robustness studies focusing on data sparsity and profile injection attacks. Instead, we propose a more holistic view of robustness for recommender systems that encompasses multiple dimensions - robustness with respect to sub-populations, transformations, distributional disparity, attack, and data sparsity. While there are several libraries that allow users to compare different recommender system models, there is no software library for comprehensive robustness evaluation of recommender system models under different scenarios. As our main contribution, we present a robustness evaluation toolkit, Robustness Gym for RecSys (RGRecSys -- https://www.github.com/salesforce/RGRecSys), that allows us to quickly and uniformly evaluate the robustness of recommender system models.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 12 Jan 2022.

Builds on

18 of 28 references.

References · 18

  1. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  2. 2017Neural Collaborative Filteringcited by 1,108
  3. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  4. 2017Deep & Cross Network for Ad Click Predictionscited by 392
  5. 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
  6. 2018Fairness of Exposure in Rankingscited by 189
  7. 2016Unbiased Learning-to-Rank with Biased Feedbackcited by 155
  8. 2020RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithmscited by 137
  9. 2017Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networkscited by 126
  10. 2021Robustness Gym: Unifying the NLP Evaluation Landscapecited by 107
  11. 2021User-oriented Fairness in Recommendationcited by 75
  12. 2020GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detectioncited by 53
6 more
  1. 2020Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learningcited by 40
  2. 2020Neural Collaborative Reasoningcited by 37
  3. 2020Correcting for Selection Bias in Learning-to-rank Systemscited by 35
  4. 2018Spectral Collaborative Filteringcited by 34
  5. 2019From Zero-Shot Learning to Cold-Start Recommendationcited by 29
  6. 2020Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shiftingcited by 6

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 2; 2025: 3.

10 papers.

  1. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  2. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  3. 2023Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Trainingcited by 2
  4. 2023Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbationscited by 2
  5. 2025Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendationscited by 2
  6. 2022Towards Robust Recommender Systems via Triple Cooperative Defensecited by 1
  7. 2024KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?cited by 1
  8. 2025Explainable Recommendation with Simulated Human Feedbackcited by 1
  9. 2024Securing Recommender System via Cooperative Trainingcited by 0
  10. 2025Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applicationscited by 0