User-oriented Fairness in Recommendation

By Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, Yongfeng Zhang. The Web Conference, 2021

301 citations recorded by the source corpus.

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.

Subfields: Computer Science Applications, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4b722f4ca16c475b5ceadb0980df9cb2d462e088

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Xiangnan He
2. Yiqun Liu
3. Shaoping Ma

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Fuli Feng
4. Meng Wang
5. Yong Li
6. Chen Gao
7. Depeng Jin
8. Tat-seng Chua

In Management Science and Operations Research

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Fuli Feng
5. Jiawei Chen
6. Hande Dong
7. Yiqun Liu
8. Yong Li

Citations in the corpus

• Bias and Debias in Recommender System: A Survey and Future Directions (2020, background): “Analogously, DetCons and DetConstSort [57] formalize the fairness as a desired distribution over sensitive attributes, and re-rank candidates ( i.e., LinkedIn users) to satisfy the constraints; Li et al. [98] constrain the difference of the average recommendation performance between two groups, and…”
• A Survey on the Fairness of Recommender Systems (2022, methodology): “Grouped by Concept Consistent Fairness CO similar individuals / different groups should receive similar outcomes [30, 36, 39, 48, 53, 54, 91, 93, 95, 97] [6, 28, 45, 68, 73, 86, 90, 107, 112, 116] [12, 22, 25, 43, 44, 52, 64, 85, 104, 114] [8, 20, 58, 60, 61, 75, 76, 78, 83, 94, 102] [82, 99, 106]”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, background): “…can be divided into two categories [4, 98, 128]: user fairness, which attempts to ensure no algorithmic bias among speciic users or demographic groups [9, 90, 96], and item fairness, which indicates fair exposures of diferent items, or no popularity bias among diferent items [2, 3, 98, 128].”