Fairness Aware Recommender System

Fairness-aware recommender systems aim to mitigate biases in recommendation algorithms that can lead to unfair outcomes for certain user groups. Current research focuses on developing methods, such as those employing diffusion models and graph-based approaches, to reduce the influence of sensitive attributes (e.g., gender, race) while maintaining recommendation accuracy. This field is crucial for building trustworthy and equitable recommendation systems across various applications, addressing concerns about algorithmic discrimination and promoting inclusivity in areas like e-commerce, healthcare, and public art curation. A key challenge involves balancing fairness with the accuracy and personalization users expect.