ADVANCEMENTS IN REDUCING BIAS IN RECOMMENDATION SYSTEMS: A TECHNICAL OVERVIEW
Keywords:
Recommendation Systems, Algorithmic Bias, Fairness Metrics, RBEO (Ranking-based Equal Opportunity), Post-processing AdjustmentsAbstract
This article explores recent advancements in addressing bias within recommendation systems, focusing on three key approaches: Ranking-based Equal Opportunity (RBEO), post-processing adjustments, and future challenges in implementation. The article examines how these methods effectively reduce demographic disparities while maintaining recommendation quality across various platforms. It investigates the technical architecture of RBEO, the flexibility of post-processing techniques, and the complex balance between fairness and system performance. The article also addresses critical challenges in scaling bias mitigation techniques and managing ethical considerations in algorithmic decision-making, providing insights into future directions for developing more equitable recommendation systems.
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