User-oriented Fairness in Recommendation

TL;DR

This paper identifies user activity bias as a source of unfairness in recommender systems and proposes a re-ranking method to improve fairness and overall performance across user groups.

Contribution

It introduces a user activity-based fairness analysis and a re-ranking approach to mitigate bias, enhancing both fairness and recommendation quality.

Findings

Active users receive higher recommendation quality than inactive users.

The proposed re-ranking method improves fairness between user groups.

Overall recommendation performance is enhanced by the approach.

Abstract

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.