User-oriented Fairness in Recommendation
This addresses fairness issues for users in recommendation systems, particularly benefiting inactive users who are the majority, but it is incremental as it builds on existing re-ranking methods.
The paper tackles unfairness in recommender systems by grouping users into advantaged (active) and disadvantaged (inactive) groups, showing that current systems provide much higher recommendation quality to advantaged users, and proposes a re-ranking approach that improves group fairness and overall performance on real-world datasets.
As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects the system performance. Therefore, it is important to identify and solve the unfairness issues in recommendation scenarios. In this paper, we address the unfairness problem in recommender systems from the user perspective. We group users into advantaged and disadvantaged groups according to their level of activity, and conduct experiments to show that current recommender systems will behave unfairly between two groups of users. Specifically, the advantaged users (active) who only account for a small proportion in data enjoy much higher recommendation quality than those disadvantaged users (inactive). Such bias can also affect the overall performance since the disadvantaged users are the majority. To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics. The experiments we conducted on several real-world datasets with various recommendation algorithms show that our approach can not only improve group fairness of users in recommender systems, but also achieve better overall recommendation performance.