Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Technical paper recommendation: a study in combining multiple information sources
Journal of Artificial Intelligence Research
Collaborative future event recommendation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Evaluating, combining and generalizing recommendations with prerequisites
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
ACO-GA approach to paper-reviewer assignment problem in CMS
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
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We present a recommender systems approach to conference paper assignment, i.e., the task of assigning paper submissions to reviewers. We address both the modeling of reviewer-paper preferences (which can be cast as a learning problem) and the optimization of reviewing assignments to satisfy global conference criteria (which can be viewed as constraint satisfaction). Due to the paucity of preference data per reviewer or per paper (relative to other recommender systems applications) we show how we can integrate multiple sources of information to learn reviewer-paper preference models. Our models are evaluated not just in terms of prediction accuracy but in terms of end-assignment quality. Using a linear programming-based assignment optimization, 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 bidding data from the IEEE ICDM 2007 conference.