AI

Online Matching with Stochastic Rewards

Abstract

The online matching problem has received significant attention in recent years because of its connections to allocation problems in Internet advertising, crowd-sourcing, etc. In these real-world applications, the typical goal is not to maximize the number of allocations, rather it is to maximize the number of successful allocations, where success of an allocation is governed by a stochastic process which follows the allocation. To address such applications, we propose and study the online matching problem with stochastic rewards (called the Online Stochastic Matching problem) in this paper. Our problem also has close connections to the existing literature on stochastic packing problems, in fact, our work initiates the study of online stochastic packing problems. We give a deterministic algorithm for the Online Stochastic Matching problem whose competitive ratio converges to (approximately) 0.567 for uniform and vanishing probabilities. We also give a randomized algorithm which outperforms the deterministic algorithm for higher probabilities. Finally, we complement our algorithms by giving an upper bound on the competitive ratio of any algorithm for this problem. This result shows that the best achievable competitive ratio for the Online Stochastic Matching problem is provably worse than that for the (non-stochastic) online matching problem.