Mitigating Bias in E-commerce Recommendation Systems: A Causal Inference Approach

Abstract

Recommendation systems play a crucial role in e-commerce platforms, significantly influencing user experiences and business outcomes. However, these systems often suffer from various biases, leading to suboptimal recommendations and potential unfairness. This paper presents a comprehensive framework for leveraging causal inference techniques to debias recommendation systems in e-commerce contexts. By integrating structural causal models, counterfactual reasoning, and interventional methods, we propose a robust approach to identify and mitigate different types of biases in recommender systems. Our methodology encompasses bias detection, causal model construction, counterfactual generation, and debiased ranking algorithms. The proposed framework aims to improve recommendation fairness and accuracy while maintaining business relevance. This research contributes to the fields of recommender systems and causal inference, providing practitioners with advanced tools for developing more equitable and effective e-commerce platforms.