Towards Long-term Fairness in Recommendation (2101.03584v1)

Abstract: As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior approaches to fairness-aware recommendation have been situated in a static or one-shot setting, where the protected groups of items are fixed, and the model provides a one-time fairness solution based on fairness-constrained optimization. This fails to consider the dynamic nature of the recommender systems, where attributes such as item popularity may change over time due to the recommendation policy and user engagement. For example, products that were once popular may become no longer popular, and vice versa. As a result, the system that aims to maintain long-term fairness on the item exposure in different popularity groups must accommodate this change in a timely fashion. Novel to this work, we explore the problem of long-term fairness in recommendation and accomplish the problem through dynamic fairness learning. We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process. We tackle this problem by proposing a fairness-constrained reinforcement learning algorithm for recommendation, which models the recommendation problem as a Constrained Markov Decision Process (CMDP), so that the model can dynamically adjust its recommendation policy to make sure the fairness requirement is always satisfied when the environment changes. Experiments on several real-world datasets verify our framework's superiority in terms of recommendation performance, short-term fairness, and long-term fairness.

Long-term Fairness in Recommendation

The paper "Long-term Fairness in Recommendation" addresses the challenge of achieving fairness in recommender systems, particularly over the long term. Traditional approaches to fairness in recommendation are often static, focusing only on immediate impacts and thereby neglecting the dynamic nature of real-world interactions and recommendations. Many existing methods operate under the assumption that protected attributes or item groupings are fixed, an approach that fails to account for potential changes over time, such as shifts in item popularity resulting from user engagement and recommendation exposure.

Dynamic Fairness in Recommender Systems

The authors introduce the concept of long-term fairness by incorporating the dynamic changes in item group labels, specifically those related to popularity. These group labels are not permanent; instead, they evolve as items receive varying levels of exposure through recommendations and user interactions. This paper proposes modeling such dynamics using Constrained Markov Decision Processes (CMDPs) to ensure that fairness constraints, such as demographic parity, are maintained iteratively.

Methodology and Experiments

The proposed framework leverages constrained reinforcement learning through a method named Constrained Policy Optimization (CPO), which adapts policies in a manner that addresses these evolving fairness constraints. The experiments conducted on several real-world datasets demonstrate the advantage of this dynamic approach over static fairness models. Notably, this framework better balances recommendation accuracy and fairness, both in short-term and long-term settings. The empirical results exhibited in the experiments show that the proposed method significantly improves both fairness measures, such as Gini index and the proportion of long-tail items being recommended, without overly compromising the recommendation performance, measured by metrics like NDCG and F1 score.

Implications and Future Directions

The research presented in this paper has substantial implications for the design of recommender systems, suggesting that incorporating long-term dynamics into fairness strategies can significantly enhance the equitable treatment of items within different popularity groups. The adaptive nature of the CMDP approach is promising for dealing with other dynamic attributes beyond popularity, such as shifts in user preferences or content types.

Looking forward, further exploration is warranted into individual fairness constraints, where focus shifts from group-based metrics to the fairness perceived by individual users or items. Moreover, extending this dynamic fairness framework to cover a broader set of constraints could benefit a wide range of recommendation scenarios, ensuring fair and equitable outcomes across various applications.