Researchers have developed a new data-driven algorithm to optimize dynamic assortment problems on two-sided service platforms. This algorithm addresses the challenge of incomplete information by learning the choice-model parameters of both customers and sellers over time. The approach is designed for discrete-time settings where customers select sellers and sellers then choose customers, with the platform aiming to maximize its objective. Performance is measured by regret, and the algorithm achieves a polylogarithmic growth rate, matching a derived lower bound for rate optimality. AI
RANK_REASON The cluster contains an academic paper detailing a new algorithm for a specific problem domain. [lever_c_demoted from research: ic=1 ai=1.0]
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →