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FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00146-020-00950-yopenalex-snapshot

Recommender Systems and their Ethical Challenges

inferredcites →https://doi.org/10.1007/s10115-011-0463-8openalex-snapshot

Data preprocessing techniques for classification without discrimination

inferredcites →https://doi.org/10.1007/s10618-010-0190-xopenalex-snapshot

Three naive Bayes approaches for discrimination-free classification

inferredcites →https://doi.org/10.1007/s10994-021-06119-yopenalex-snapshot

Stronger data poisoning attacks break data sanitization defenses

inferredcites →https://doi.org/10.1109/icdmw.2009.83openalex-snapshot

Building Classifiers with Independency Constraints

inferredcites →https://doi.org/10.1109/mc.2009.263openalex-snapshot

Matrix Factorization Techniques for Recommender Systems

inferredcites →https://doi.org/10.1109/sp.2018.00057openalex-snapshot

Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

inferredcites →https://doi.org/10.1145/2090236.2090255openalex-snapshot

Fairness through awareness

inferredcites →https://doi.org/10.1145/3038912.3052660openalex-snapshot

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

inferredcites →https://doi.org/10.1145/3109859.3109887openalex-snapshot

Fairness-Aware Group Recommendation with Pareto-Efficiency

inferredcites →https://doi.org/10.1145/3132847.3132938openalex-snapshot

FA*IR

inferredcites →https://doi.org/10.1145/3219819.3220046openalex-snapshot

A Unified Approach to Quantifying Algorithmic Unfairness

inferredcites →https://doi.org/10.1145/3240323.3240372openalex-snapshot

Calibrated recommendations

inferredcites →https://doi.org/10.1145/3274694.3274706openalex-snapshot

Poisoning Attacks to Graph-Based Recommender Systems

inferredcites →https://doi.org/10.1145/3289600.3291002openalex-snapshot

Fighting Fire with Fire

inferredcites →https://doi.org/10.1145/3292500.3330745openalex-snapshot

Fairness in Recommendation Ranking through Pairwise Comparisons

inferredcites →https://doi.org/10.1145/3336191.3371855openalex-snapshot

Addressing Marketing Bias in Product Recommendations

inferredcites →https://doi.org/10.1145/3351095.3372864openalex-snapshot

On the apparent conflict between individual and group fairness

inferredcites →https://doi.org/10.1145/3366423.3380072openalex-snapshot

Influence Function based Data Poisoning Attacks to Top-N Recommender Systems

inferredcites →https://doi.org/10.1145/3366423.3380196openalex-snapshot

FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

inferredcites →https://doi.org/10.1145/3366424.3383557openalex-snapshot

Fairness of Classification Using Users’ Social Relationships in Online Peer-To-Peer Lending

inferredcites →https://doi.org/10.1145/3368089.3409704openalex-snapshot

Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness

inferredcites →https://doi.org/10.1145/3394486.3403080openalex-snapshot

InFoRM: Individual Fairness on Graph Mining

inferredcites →https://doi.org/10.1145/3394486.3403102openalex-snapshot

Towards Fair Truth Discovery from Biased Crowdsourced Answers

inferredcites →https://doi.org/10.1145/3397271.3401051openalex-snapshot

Fairness-Aware Explainable Recommendation over Knowledge Graphs

inferredcites →https://doi.org/10.1145/3397271.3401100openalex-snapshot

Controlling Fairness and Bias in Dynamic Learning-to-Rank

inferredcites →https://doi.org/10.1145/3397271.3401177openalex-snapshot

Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems

inferredcites →https://doi.org/10.1145/3437963.3441820openalex-snapshot

Popularity-Opportunity Bias in Collaborative Filtering

inferredcites →https://doi.org/10.1145/3442188.3445915openalex-snapshot

Fair Classification with Group-Dependent Label Noise

inferredcites →https://doi.org/10.1145/3442381.3449904openalex-snapshot

Debiasing Career Recommendations with Neural Fair Collaborative Filtering

inferredcites →https://doi.org/10.1145/3442381.3450015openalex-snapshot

Learning Fair Representations for Recommendation: A Graph-based Perspective

inferredcites →https://doi.org/10.1145/3442381.3450066openalex-snapshot

Data Poisoning Attacks and Defenses to Crowdsourcing Systems

inferredcites →https://doi.org/10.1145/3442381.3450080openalex-snapshot

Estimation of Fair Ranking Metrics with Incomplete Judgments

inferredcites →https://doi.org/10.1145/3447548.3467249openalex-snapshot

Deconfounded Recommendation for Alleviating Bias Amplification

inferredcites →https://doi.org/10.1145/3447548.3467335openalex-snapshot

Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems

inferredcites →https://doi.org/10.1145/3447548.3467349openalex-snapshot

Maxmin-Fair Ranking

inferredcites →https://doi.org/10.1145/3448016.3452787openalex-snapshot

OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning

inferredcites →https://doi.org/10.1145/3460120.3485368openalex-snapshot

Subpopulation Data Poisoning Attacks

inferredcites →https://doi.org/10.1145/3461702.3462559openalex-snapshot

FaiR-N: Fair and Robust Neural Networks for Structured Data

inferredcites →https://doi.org/10.1177/0049124118782533openalex-snapshot

Fairness in Criminal Justice Risk Assessments: The State of the Art

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4469569openalex-extracted

A Survey on Fairness-Aware Recommender Systems

inferred← citeshttps://doi.org/10.1007/s10462-023-10663-5openalex-snapshot

Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation

inferred← citeshttps://doi.org/10.1109/access.2024.3368058openalex-snapshot

Recommender System in Academic Choices of Higher Education: A Systematic Review

inferred← citeshttps://doi.org/10.23919/wiopt71098.2026.11568230openalex-snapshot

When the Server Steps In: Calibrated Updates for Fair Federated Learning

inferred← citeshttps://doi.org/10.31979/etd.ggt3-adtqopenalex-snapshot

Synthetic Data Generation for Accurate, Fair, and Private Recommender Systems

inferred← citeshttps://doi.org/10.3390/info14020131openalex-snapshot

Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems

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