Conference paper assignment---the task of assigning paper submissions to reviewers---is a key step in the management and smooth functioning of conferences. We study this problem as an application of recommender systems research. Besides the traditional goal of predicting `who likes what?', a conference management system must take into account 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. Issues of modeling preferences and tastes in reviewing have traditionally been studied separately from the optimization of assignments. In this thesis, we present an integrated study of both aspects. First, due to the sparsity of data (relative to other recommender systems applications), we integrate multiple sources of information to learn reviewer/paper preference models, using methods commonly associated with merging content-based and collaborative filtering in the study of large recommender systems. Second, our models are evaluated not just in terms of prediction accuracy, but also in terms of end-assignment quality, and considering multiple evaluation criteria. Using a linear programming-based assignment optimization formulation, we show how our approach better explores the space of potential assignments to maximize the overall affinities of papers assigned to reviewers. Finally, we demonstrate encouraging results on real reviewer preference data gathered during the IEEE ICDM 2007 conference, a premier international data mining conference. Our research demonstrates that there are significant advantages to applying recommender system concepts to the conference paper assignment problem.