User-oriented Fairness in Recommendation (2104.10671v1)

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.

A Perspective on User-oriented Fairness in Recommender Systems

Abstract and Introduction:

The paper "User-oriented Fairness in Recommendation" addresses the challenge of fairness in recommender systems by focusing on users categorized into advantaged and disadvantaged groups according to their activity levels. The authors identify that current recommendation algorithms exhibit bias that favors more active users (the advantaged group), worsening the experience for less active users (the disadvantaged group), who form the majority. The paper proposes a novel re-ranking framework to mitigate this imbalance by introducing constraints designed to achieve fairer outcomes across different user groups, thereby also improving the system's overall performance.

Motivating Fairness Concerns:

The paper immerses itself in the ongoing dialogue on algorithmic fairness by spotlighting an often-overlooked dimension: the disparity in recommendation quality between user groups characterized by their level of platform engagement. Earlier works have largely focused on reducing item-side popularity bias. This paper, however, broadens the scope by considering the user side—a critical viewpoint given that disadvantaged users are underrepresented in datasets, causing the trained models to skew heavily in favor of the advantaged minority. Empirical analyses reveal that datasets from Amazon—across categories like Beauty, Grocery, and Health—display significant differences in engagement patterns, corroborating the hypothesis of systemic bias favoring active users.

Methodology and Framework:

To address this, the authors devise a fairness-aware re-ranking algorithm that's model-agnostic, offering flexibility to be integrated with any baseline recommender system. The approach involves constructing post-hoc fairness constraints to balance the recommendation quality between user groups, aiming for what they term "user-oriented group fairness." The solution is formalized as a 0-1 integer programming problem that seeks to maximize preference scores while adhering to fairness constraints. This re-ranking method, when applied to leading recommendation algorithms like Matrix Factorization (MF) and Neural Matrix Factorization (NeuMF), demonstrates an ability to substantially reduce the discord in recommendation quality between user groups.

Empirical Results:

Experiments on three distinct Amazon review datasets show that the proposed method effectively enhances fairness by narrowing the performance gap between user groups, evident in metrics like F1@10 and NDCG@10. Notably, the results exhibit that the re-ranking mechanism not only ameliorates fairness it but does so without compromising, and often improving, the overall recommendation performance. For instance, when employing the NeuMF algorithm on the Grocery dataset, the fairness-augmenting technique elevates both fairness and overall recommendation efficacy. Furthermore, this re-balancing positively impacts the recommendation outcome for the disadvantaged users—indicative of more equitable results.

Implications and Future Work:

The paper's findings hold consequential implications. The proposed framework offers a nuanced understanding of performance disparities tied to user activity levels, presenting a pathway that complements existing methodologies focusing largely on item-side fairness. Practically, the improved recommendation performance for the vast yet underserved segment of inactive users hints at commercial and user satisfaction dividends. Theoretically, this approach inspires further investigation into multi-faceted bias mitigation in recommendation systems and opens up avenues for incorporating joint item-user side fairness.

In future developments, extending this framework to dynamically accommodate shifts in user activity patterns and preferences could yield deeper insights and improvements. Additionally, integrating this work with other fairness metrics and constraints pertaining to user demographics or item attributes could be a compelling research trajectory, enhancing the robustness and applicability of fairness strategies in evolving recommender systems.