Personalized Counterfactual Fairness in Recommendation

Abstract

Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness problems in recommendations. Just like users have personalized preferences on items, users' demands for fairness are also personalized in many scenarios. Therefore, it is important to provide personalized fair recommendations for users to satisfy their personalized fairness demands. Besides, previous works on fair recommendation mainly focus on association-based fairness. However, it is important to advance from associative fairness notions to causal fairness notions for assessing fairness more properly in recommender systems. Based on the above considerations, this paper focuses on achieving personalized counterfactual fairness for users in recommender systems. To this end, we introduce a framework for achieving counterfactually fair recommendations through adversary learning by generating feature-independent user embeddings for recommendation. The framework allows recommender systems to achieve personalized fairness for users while also covering non-personalized situations. Experiments on two real-world datasets with shallow and deep recommendation algorithms show that our method can generate fairer recommendations for users with a desirable recommendation performance.

10 pages. Accepted to ACM SIGIR 2021

References in corpus (24)

• Adam: A Method for Stochastic Optimization
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Equality of Opportunity in Supervised Learning
• Counterfactual Fairness
• Fairness of Exposure in Rankings
• Equity of Attention: Amortizing Individual Fairness in Rankings
• Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
• User-oriented Fairness in Recommendation
• Towards Long-term Fairness in Recommendation
• FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
• Avoiding Discrimination through Causal Reasoning
• Learning Non-Discriminatory Predictors
• Beyond Parity: Fairness Objectives for Collaborative Filtering
• Managing Popularity Bias in Recommender Systems with Personalized Re-ranking
• A Reductions Approach to Fair Classification
• Compositional Fairness Constraints for Graph Embeddings
• Multisided Fairness for Recommendation
• Neural Collaborative Reasoning
• Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
• PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
• Policy Learning for Fairness in Ranking
• Survey on Causal-based Machine Learning Fairness Notions
• Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness
• Neural Logic Reasoning

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• A Survey on the Fairness of Recommender Systems
• Fairness in Recommender Systems: Research Landscape and Future Directions
• Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
• Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning
• User-controllable Recommendation Against Filter Bubbles
• Explainable Fairness in Recommendation
• Unlearning Protected User Attributes in Recommendations with Adversarial Training
• Deconfounded Causal Collaborative Filtering
• Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations
• DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias
• Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective
• Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation
• Dynamic Causal Collaborative Filtering
• Intersectional Two-sided Fairness in Recommendation
• Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models
• Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness
• Causality-Inspired Fair Representation Learning for Multimodal Recommendation
• Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
• FARA: Future-aware Ranking Algorithm for Fairness Optimization
• Automatic Feature Fairness in Recommendation via Adversaries
• Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)
• Problem Learning: Towards the Free Will of Machines