The field of recommendation systems and fairness is moving towards developing more effective and efficient methods for handling high-dimensional and sparse data. Researchers are exploring new approaches to improve the accuracy and fairness of recommendation systems, including the use of singular value decomposition and local collaborative filtering. Additionally, there is a growing focus on designing local differential privacy mechanisms that reduce data unfairness and improve fairness in downstream classification. Notable papers in this area include:

• A paper that proposes a group recommender system using soft-impute singular value decomposition, which outperforms baseline methods in recall for small user groups.
• A paper that investigates optimal fairness under local differential privacy and develops a tractable optimization framework for multi-valued attributes, demonstrating a direct link between privacy-aware pre-processing and classification fairness.
• A paper that proposes a fairness-oriented low rank factorization framework, leveraging singular value decomposition to improve deep learning model fairness, which outperforms conventional methods and state-of-the-art fairness-enhancing techniques.