Development of an Equity Strategy for Recommendation Systems

As a highly data-driven application, recommender systems can be affected by data distortions, culminating in unfair results for different groups of data, which can be a reason to affect system performance. Therefore, it is important to identify and resolve issues of unfairness in referral scenarios. We therefore developed an equity algorithm aimed at reducing group injustice in recommender systems. The algorithm was tested on two existing datasets (MovieLens and Songs) with two user clustering strategies. We were able to reduce group unfairness in both data sets by considering the two clustering strategies.