  Today's online platforms heavily lean on algorithmic recommendations for
bolstering user engagement and driving revenue. However, these recommendations
can impact multiple stakeholders simultaneously -- the platform, items
(sellers), and users (customers) -- each with their unique objectives, making
it difficult to find the right middle ground that accommodates all
stakeholders. To address this, we introduce a novel fair recommendation
framework, Problem (FAIR), that flexibly balances multi-stakeholder interests
via a constrained optimization formulation. We next explore Problem (FAIR) in a
dynamic online setting where data uncertainty further adds complexity, and
propose a low-regret algorithm FORM that concurrently performs real-time
learning and fair recommendations, two tasks that are often at odds. Via both
theoretical analysis and a numerical case study on real-world data, we
demonstrate the efficacy of our framework and method in maintaining platform
revenue while ensuring desired levels of fairness for both items and users.
