Two Sided

Two-sided markets, encompassing platforms connecting distinct groups like buyers and sellers or users and creators, are a central focus of current research. Key objectives include optimizing resource allocation, designing fair ranking and recommendation systems, and mitigating issues like algorithmic collusion and bias. Researchers employ various approaches, including reinforcement learning (with architectures like Soft Actor-Critic and convolutional neural networks), inverse propensity weighting, and the Sinkhorn algorithm, to address these challenges. This research significantly impacts the design and operation of online marketplaces and recommender systems, improving efficiency, fairness, and user experience.