<p>We study matching settings in which a set of agents have private utilities over a set of items. Each agent reports a partition of the items into approval sets of different threshold utility levels. Given this limited information on input, the goal is to compute an assignment of the items to the agents (subject to cardinality constraints depending on the application) that (approximately) maximizes the social welfare (the total utility of the agents for their assigned items). We first consider the well-known, simple one-sided matching problem in which each of <i>n</i> agents is to be assigned exactly one of <i>n</i> items. We show that with <i>t</i> threshold utility levels, the distortion of deterministic matching algorithms is <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\Theta (\root t \of {n})\)</EquationSource> </InlineEquation> while that of randomized algorithms is <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\Theta (\root t+1 \of {n})\)</EquationSource> </InlineEquation>. We then show that our distortion bounds extend to a more general setting in which there are multiple copies of the items, each agent can be assigned a number of items (even copies of the same one) up to a capacity, and the utility of an agent for an item depends on the number of its copies that the agent is given.</p>