We investigate the following problem: given a set of jobs and a set of people with preferences over the jobs, what is the optimal way of matching people to jobs? Here we consider the notion of popularity. A matching M is popular if there is no matching M such that more people prefer M to M than the other way around. Determining whether a given instance admits a popular matching and, if so, finding one, was studied by Abraham et al. (SIAM J. Comput. 37(4):1030-1045, 2007). If there is no popular matching, a reasonable substitute is a matching whose unpopularity is bounded. We consider two measures of unpopularity-unpopularity factor denoted by u(M) and unpopularity margin denoted by g(M). McCutchen recently showed that computing a matching M with the minimum value of u(M) or g(M) is NP-hard, and that if G does not admit a popular matching, then we have u(M)≥2 for all matchings M in G. Here we show that a matching M that achieves u(M)=2 can be computed in O(m\sqrt n) time (where m is the number of edges in G and n is the number of nodes) provided a certain graph H admits a matching that matches all people. We also describe a sequence of graphs: H=H 2,H 3,⋯,H k such that if H k admits a matching that matches all people, then we can compute in O(km\sqrt n) time a matching M such that u(M)≤k-1 and g(M)\le n(1-\frac2 k). Simulation results suggest that our algorithm finds a matching with low unpopularity in random instances. © 2010 Springer Science+Business Media, LLC.