Large-scale Robust Online Matching and Its Application in E-commerce

Abstract

This talk will be focused on large-scale matching problem that aims to find the optimal assignment of tasks to different agents under linear constraints. Large-scale matching has found numerous applications in e-commerce. An well known example is budget aware online advertisement. A common practice in online advertisement is to find, for each opportunity or user, the advertisements that fit best with his/her interests. The main shortcoming with this greedy approach is that it did not take into account the budget limits set by advertisers. Our studies, as well as others, have shown that by carefully taking into budget limits of individual advertisers, we could significantly improve the performance of the advertisement system. Despite of rich literature, two important issues are often overlooked in the previous studies of matching/assignment problem. The first issues arises from the fact that most quantities used by optimization are estimated based on historical data and therefore are likely to be inaccurate and unreliable. The second challenge is how to perform online matching as in many e-commerce problems, tasks are created in an online fashion and algorithm has to make assignment decision immediately when every task emerges. We refer to these two issues as challenges of "robust matching" and "online matching". To address the first challenge, I will introduce two different techniques for robust matching. The first approach is based on the theory of robust optimization that takes into account the uncertainties of estimated quantities when performing optimization. The second approach is based on the theory of two-sided matching whose result only depends on the partial preference of estimated quantities. To deal with the challenge of online matching, I will discuss two online optimization techniques, one based on theory of primal-dual online optimization and one based on minimizing dynamic regret under long term constraints. We verify the effectiveness of all these approaches by applying them to real-world projects developed in Alibaba.