A Framework for Optimizing Paper Matching

Laurent Charlin, Richard S. Zemel, Craig Boutilier
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:114-123, 2011.

Abstract

At the heart of many scientific conferences is the problem of matching submitted papers to suitable reviewers. Arriving at a good assignment is a major and important challenge for any conference organizer. In this paper we propose a framework to optimize paper-to-reviewer assignments. Our framework uses suitability scores to measure pairwise affinity between papers and reviewers. We show how learning can be used to infer suitability scores from a small set of provided scores, thereby reducing the burden on reviewers and organizers. We frame the assignment problem as an integer program and propose several variations for the paper-to-reviewer matching domain. We also explore how learning and matching interact. Experiments on two conference data sets examine the performance of several learning methods as well as the effectiveness of the matching formulations.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR9-charlin11a, title = {A Framework for Optimizing Paper Matching}, author = {Charlin, Laurent and Zemel, Richard S. and Boutilier, Craig}, booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence}, pages = {114--123}, year = {2011}, editor = {Cozman, Fabio and Pfeffer, Avi}, volume = {R9}, series = {Proceedings of Machine Learning Research}, month = {14--17 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r9/main/assets/charlin11a/charlin11a.pdf}, url = {https://proceedings.mlr.press/r9/charlin11a.html}, abstract = {At the heart of many scientific conferences is the problem of matching submitted papers to suitable reviewers. Arriving at a good assignment is a major and important challenge for any conference organizer. In this paper we propose a framework to optimize paper-to-reviewer assignments. Our framework uses suitability scores to measure pairwise affinity between papers and reviewers. We show how learning can be used to infer suitability scores from a small set of provided scores, thereby reducing the burden on reviewers and organizers. We frame the assignment problem as an integer program and propose several variations for the paper-to-reviewer matching domain. We also explore how learning and matching interact. Experiments on two conference data sets examine the performance of several learning methods as well as the effectiveness of the matching formulations.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T A Framework for Optimizing Paper Matching %A Laurent Charlin %A Richard S. Zemel %A Craig Boutilier %B Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2011 %E Fabio Cozman %E Avi Pfeffer %F pmlr-vR9-charlin11a %I PMLR %P 114--123 %U https://proceedings.mlr.press/r9/charlin11a.html %V R9 %X At the heart of many scientific conferences is the problem of matching submitted papers to suitable reviewers. Arriving at a good assignment is a major and important challenge for any conference organizer. In this paper we propose a framework to optimize paper-to-reviewer assignments. Our framework uses suitability scores to measure pairwise affinity between papers and reviewers. We show how learning can be used to infer suitability scores from a small set of provided scores, thereby reducing the burden on reviewers and organizers. We frame the assignment problem as an integer program and propose several variations for the paper-to-reviewer matching domain. We also explore how learning and matching interact. Experiments on two conference data sets examine the performance of several learning methods as well as the effectiveness of the matching formulations. %Z Reissued by PMLR on 04 October 2026.
APA
Charlin, L., Zemel, R.S. & Boutilier, C.. (2011). A Framework for Optimizing Paper Matching. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R9:114-123 Available from https://proceedings.mlr.press/r9/charlin11a.html. Reissued by PMLR on 04 October 2026.

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