Researchers Develop Algorithm for Two-Sided Platform Matching with Unknown Customer and Seller Preferences
Computer scientists have developed a data-driven algorithm that optimizes seller assortments on two-sided platforms while learning customer and seller preferences in real time. The algorithm addresses a gap in existing research by handling incomplete information about both sides of the marketplace simultaneously. The work is significant because it provides theoretically optimal solutions for a common problem faced by online platforms like ride-sharing and freelance marketplaces.
A new study on arXiv presents an algorithm for dynamic assortment optimization on two-sided service platforms where the platform operator lacks advance knowledge of customer and seller preferences. In each period, customers arrive and select from a displayed assortment of sellers using a multinomial logit choice model, then sellers review proposals and select customers using their own preference model. The key innovation is that the algorithm simultaneously learns both sides' unknown choice parameters while optimizing platform revenue over time. The researchers prove that their approach achieves polylogarithmic regret growth—meaning revenue loss compared to a hypothetical scenario with perfect information—and demonstrate this rate is theoretically optimal through matching lower bounds. This represents the first study to address dynamic assortment with unknown parameters on both sides of a platform.
What's missing
The paper does not discuss computational complexity or practical implementation considerations beyond theoretical regret bounds. Real-world applicability to specific platform types (e.g., ride-sharing, labor marketplaces) and comparison with existing industry practices are not addressed.
What different sources said
- arXiv cs.LGCenter
Learn to Match: Two-Sided Matching with Temporally Extended Feedback
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