Popularity Bias

Popularity bias in recommender systems, where popular items disproportionately dominate recommendations, is a significant research area aiming to create fairer and more diverse systems. Current research focuses on mitigating this bias through various techniques, including modifying existing recommender architectures (e.g., collaborative filtering, matrix factorization) and employing novel approaches like contrastive learning and causal inference to disentangle item quality from popularity. Addressing popularity bias is crucial for improving the accuracy and diversity of recommendations, promoting equitable exposure for all items, and enhancing the overall user experience across various applications, from online content platforms to music streaming services.