Recommender systems have become an integral part of our daily lives, powering the personalized recommendations that we receive on social media, e-commerce platforms, and streaming services. These systems are designed to make our lives easier by suggesting products, services, and content that are relevant to our interests and preferences. However, as powerful as these systems are, they are not perfect, and there are concerns about their fairness, especially in terms of how they impact marginalized groups.
In this article, we will explore the concept of fairness in recommender systems, the challenges involved in achieving fairness, and the approaches that have been proposed to address these challenges.