A Research Proposal for Promoting Fairness in E-Commerce Recommendation Systems

Personalizing content is essential to optimizing consumer experiences in e-commerce recommendation systems. Yet, the inadvertently introduced biases in the process can lead to unfair treatment of less active or economically diverse user groups. To address this issue, this research aims to develop a method that utilizes non-sensitive criteria for user classification, enhancing fairness without relying on demographic profiling. The method comprises three stages: 1) formulating user grouping strategies based on activity levels, 2) establishing specific group-wise fairness metrics and constraints, and 3) implementing a fairness-aware ranking algorithm to balance recommendation accuracy with overall fairness. Integrating ethical algorithm design with practical interventions, the proposed method strives for improved fairness in digital commerce and more equitable access to recommendations across diverse user groups. Overall, the research is expected to establish a more fairness-aware framework for e-commerce recommendation systems, which facilitates transparency and justice in digital marketplaces and, ultimately, a more inclusive online environment.