  Automated reviewer recommendation for scientific conferences currently relies
on the assumption that the program committee has the necessary expertise to
handle all submissions. However, topical discrepancies between received
submissions and reviewer candidates might lead to unreliable reviews or
overburdening of reviewers, and may result in the rejection of high-quality
papers. In this work, we present DiveRS, an explainable flow-based reviewer
assignment approach, which automatically generates reviewer assignments as well
as suggestions for extending the current program committee with new reviewer
candidates. Our algorithm focuses on the diversity of the set of reviewers
assigned to papers, which has been mostly disregarded in prior work.
Specifically, we consider diversity in terms of professional background,
location and seniority. Using two real world conference datasets for
evaluation, we show that DiveRS improves diversity compared to both real
assignments and a state-of-the-art flow-based reviewer assignment approach.
Further, based on human assessments by former PC chairs, we find that DiveRS
can effectively trade off some of the topical suitability in order to construct
more diverse reviewer assignments.
