The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	A survey on online matching and ad allocation
Author:	Lee, Ryan
View Online:	njit-etd2023-007 (ix, 48 pages ~ 1.1 MB pdf)
Department:	Department of Data Science
Degree:	Master of Science
Program:	Data Science
Document Type:	Thesis
Advisory Committee:	Xu, Pan (Committee chair) Shang, Zuofeng (Committee member) Geller, James (Committee member)
Date:	2023-05
Keywords:	Online matching Ad allocation Probabilistic algorithms Bipartite graphs Decision making Graph theory
Availability:	Unrestricted
Abstract:	One of the classical problems in graph theory is matching. Given an undirected graph, find a matching which is a set of edges without common vertices. In 1990s, Richard Karp, Umesh Vazirani, and Vijay Vazirani would be the first computer scientists to use matchings for online algorithms [8]. In our domain, an online algorithm operates in the online setting where a bipartite graph is given. On one side of the graph there is a set of advertisers and on the other side we have a set of impressions. During the online phase, multiple impressions will arrive and the objective of the online algorithm is to match incoming impressions to advertisers. The theory behind online matching is not only fascinating but has a lot of practical applications. One example is ridesharing platforms like Uber. An online algorithm can be used to assign incoming requests to available Uber drivers in order to maximize profits and fairness.