  Recommender system recommends interesting items to users based on users' past
information history. Researchers have been paying attention to improvement of
algorithmic performance such as MAE and precision@K. Major techniques such as
matrix factorization and learning to rank are optimized based on such
evaluation metrics. However, the intrinsic Matthew Effect problem poses great
threat to the fairness of the recommender system, and the unfairness problem
cannot be resolved by optimization of traditional metrics. In this paper, we
propose a novel algorithm that incorporates Matthew Effect reduction with the
matrix factorization framework. We demonstrate that our approach can boost the
fairness of the algorithm and enhances performance evaluated by traditional
metrics.
