Fairness-Aware Recommendation in Multi-Sided Platforms

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be important to optimize utilities not just for the end user, but also for other entities such as item sellers or producers who desire a fair representation of their items. Existing solutions either lack the multi-sided nature of fairness in recommendations, or do not properly address various aspects of multi-sided fairness in recommendations. In this thesis, we aim at first investigating the impact of unfair recommendations on the system and how it can negatively affect major entities in the system. Then, we seek to propose a general graph-based solution that works as a post processing approach after recommendation generation to tackle the unfairness of recommendations. We plan to perform extensive experiments to evaluate the effectiveness of the proposed approach.