线上配对市场中竞争比率与差异之权衡探讨
Exploring the Tradeoff between Competitive Ratio and Variance in Online-Matching Markets
摘要：
在本文中，我们提出了一个基于在线匹配的模型来研究在广泛的在线匹配市场中出现的分配问题，包括在线推荐，乘车平台和众包市场。它的特点是，每个分配可以请求一个随机的资源集，并产生一个随机的效用，两者（成本和效用）可以任意相互关联。我们提出了两个基于线性规划的参数化策略，研究之间的权衡\emph{competitive ratio}（CR）的总效用和\emph{variance}的总匹配数（未加权版本）。第一个（SAMP）是根据从千里眼最佳提取的分布对边缘进行采样，而第二个（ATT）具有时间自适应衰减框架，该框架导致对最先进的竞争比结果的改进。我们还考虑了一个大预算的假设下的问题，并表明，SAMP实现渐近最优性能的竞争比。
英文摘要：
In this paper, we propose an online-matching-based model to study the assignment problems arising in a wide range of online-matching markets, including online recommendations, ride-hailing platforms, and crowdsourcing markets. It features that each assignment can request a random set of resources and yield a random utility, and the two (cost and utility) can be arbitrarily correlated with each other. We present two linear-programming-based parameterized policies to study the tradeoff between the \emph{competitive ratio} (CR) on the total utilities and the \emph{variance} on the total number of matches (unweighted version). The first one (SAMP) is to sample an edge according to the distribution extracted from the clairvoyant optimal, while the second (ATT) features a time-adaptive attenuation framework that leads to an improvement over the state-of-the-art competitive-ratio result. We also consider the problem under a large-budget assumption and show that SAMP achieves asymptotically optimal performance in terms of competitive ratio.
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