Autonomous decision-making systems, particularly recommender systems, have received increasing attention concerning fairness, i.e., if all stakeholders affected by such a system are treated equally as a result of the recommendations. Existing approaches primarily focus on fairness between two stakeholders – consumers and providers or consumers and items – treating providers and items as the same entity. However, we argue for the treatment of providers and items as distinct stakeholders to offer more comprehensive models of fairness in recommender systems. To this end, we propose a fairness-aware recommender system, CIPFRS, designed to optimize fairness across all three key stakeholders: consumers, providers, and items. We examine consumer fairness regarding their level of interaction with the system; high and low-activity users should be treated equally. Further, all providers should have an equal opportunity for their products to be recommended. Finally, we propose an approach to implement item fairness in each provider’s inventory. We report an extensive evaluation of the proposed solution through three datasets, demonstrating that considering all three stakeholders yields improved recommendations while minimizing bias.

Recommender systems often aim to serve multiple stakeholders, such as consumers and providers, each with distinct fairness expectations. While fairness-aware recommendation has gained increasing attention, most existing methods assume unlimited item availability. However, real-world scenarios often involve limited supply, where only a small number of item copies can be allocated. This creates new challenges in balancing fair exposure, equitable access, and relevance. In this paper, we propose a multisided fairness-aware recommendation framework designed for settings with limited item availability. Our approach explicitly models fairness both across stakeholders, ensuring consumers and providers are treated equitably compared to their peers, and within consumer–provider relationships, ensuring stakeholders treat their counterparts fairly. We formalize these as inter- and intra-stakeholder fairness and introduce evaluation metrics that measure treatment consistency under supply constraints. To address the allocation challenge, we develop a novel algorithm that assigns limited items while jointly optimizing for fairness and relevance. We evaluate our method on real-world datasets from Amazon and Goodreads, showing that it mitigates bias toward highly active users and dominant providers. Compared to conventional recommendation algorithms, our approach reduces fairness disparities by up to 80 %, underscoring the importance of fairness-aware design in real-world, resource-constrained recommendation scenarios.