  Assigning qualified, unbiased and interested reviewers to paper submissions
is vital for maintaining the integrity and quality of the academic publishing
system and providing valuable reviews to authors. However, matching thousands
of submissions with thousands of potential reviewers within a limited time is a
daunting challenge for a conference program committee. Prior efforts based on
topic modeling have suffered from losing the specific context that help define
the topics in a publication or submission abstract. Moreover, in some cases,
topics identified are difficult to interpret. We propose an approach that
learns from each abstract published by a potential reviewer the topics studied
and the explicit context in which the reviewer studied the topics. Furthermore,
we contribute a new dataset for evaluating reviewer matching systems. Our
experiments show a significant, consistent improvement in precision when
compared with the existing methods. We also use examples to demonstrate why our
recommendations are more explainable. The new approach has been deployed
successfully at top-tier conferences in the last two years.
