  Conference paper assignment, i.e., the task of assigning paper submissions to
reviewers, presents multi-faceted issues for recommender systems research.
Besides the traditional goal of predicting `who likes what?', a conference
management system must take into account aspects such as: reviewer capacity
constraints, adequate numbers of reviews for papers, expertise modeling,
conflicts of interest, and an overall distribution of assignments that balances
reviewer preferences with conference objectives. Among these, issues of
modeling preferences and tastes in reviewing have traditionally been studied
separately from the optimization of paper-reviewer assignment. In this paper,
we present an integrated study of both these aspects. First, due to the paucity
of data per reviewer or per paper (relative to other recommender systems
applications) we show how we can integrate multiple sources of information to
learn paper-reviewer preference models. Second, our models are evaluated not
just in terms of prediction accuracy but in terms of the end-assignment
quality. Using a linear programming-based assignment optimization formulation,
we show how our approach better explores the space of unsupplied assignments to
maximize the overall affinities of papers assigned to reviewers. We demonstrate
our results on real reviewer preference data from the IEEE ICDM 2007
conference.
