Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

2021/06/25 by Weiwen Liu, Feng Liu, Liu, Weiwen +9 · 2 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Computers and Society (cs.CY) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Recommender Systems and Techniques #Stochastic Gradient Optimization Techniques

paper · pdf · doi:10.48550/arxiv.2106.13386

openalex publication_date 2021/06/25 · openalex created_date 2021/07/05 · openalex updated_date 2026/07/28

Abstract

Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders. In Interactive Recommender Systems (IRS), user preferences and the system's fairness status are constantly changing over time. Existing fairness-aware recommenders mainly consider fairness in static settings. Directly applying existing methods to IRS will result in poor recommendation. To resolve this problem, we propose a reinforcement learning based framework, FairRec, to dynamically maintain a long-term balance between accuracy and fairness in IRS. User preferences and the system's fairness status are jointly compressed into the state representation to generate recommendations. FairRec aims at maximizing our designed cumulative reward that combines accuracy and fairness. Extensive experiments validate that FairRec can improve fairness, while preserving good recommendation quality.

Citations

• Matrix Factorization Techniques for Recommender Systems
• The unequal variance t-test is an underused alternative to Student's t-test and the Mann–Whitney U test
• Continuous control with deep reinforcement learning
• Personalizing Fairness-aware Re-ranking
• Multi-stakeholder Recommendation and its Connection to Multi-sided\n Fairness

Cited by

• Revisiting Fairness-aware Interactive Recommendation: Item Lifecycle as a Control Knob
• Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model

Related

• Recommendation Fairness: From Static to Dynamic
• Fairness in Recommendation: Foundations, Methods and Applications
• FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data
• Improving Recommendation Fairness via Data Augmentation
• Experiments on Generalizability of User-Oriented Fairness in Recommender Systems