Interpolating Item and User Fairness in Multi-Sided Recommendations Qinyi Chen 1 Jason Cheuk Nam Liang 1

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.