  Matching problems are ubiquitous. They occur in economic markets, labor
markets, internet advertising, and elsewhere. In this paper we focus on an
application of matching for social media. Our goal is to distribute content
from information suppliers to information consumers. We seek to maximize the
overall relevance of the matched content from suppliers to consumers while
regulating the overall activity, e.g., ensuring that no consumer is overwhelmed
with data and that all suppliers have chances to deliver their content.
  We propose two matching algorithms, GreedyMR and StackMR, geared for the
MapReduce paradigm. Both algorithms have provable approximation guarantees, and
in practice they produce high-quality solutions. While both algorithms scale
extremely well, we can show that StackMR requires only a poly-logarithmic
number of MapReduce steps, making it an attractive option for applications with
very large datasets. We experimentally show the trade-offs between quality and
efficiency of our solutions on two large datasets coming from real-world
social-media web sites.
