Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
arXiv:2106.13386
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.
References in corpus (7)
- Continuous control with deep reinforcement learning
- Fairness of Exposure in Rankings
- Deep Reinforcement Learning for Page-wise Recommendations
- Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
- Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
- Personalizing Fairness-aware Re-ranking
- Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness