Automating the assignment of submitted manuscripts to reviewers
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Mining for proposal reviewers: lessons learned at the national science foundation
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-aspect expertise matching for review assignment
Proceedings of the 17th ACM conference on Information and knowledge management
Relevance and ranking in online dating systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Recommendation systems with complex constraints: A course recommendation perspective
ACM Transactions on Information Systems (TOIS)
Integer linear programming for Constrained Multi-Aspect Committee Review Assignment
Information Processing and Management: an International Journal
Foundations and Trends in Information Retrieval
MEET: a generalized framework for reciprocal recommender systems
Proceedings of the 21st ACM international conference on Information and knowledge management
Identifying influential scholars in academic social media platforms
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Automatic review assignment can significantly improve the productivity of many people such as conference organizers, journal editors and grant administrators. Most previous works have set the problem up as using a paper as a query to independently "retrieve" a set of reviewers that should review the paper. A more appropriate formulation of the problem would be to simultaneously optimize the assignments of all the papers to an entire committee of reviewers under constraints such as the review quota. In this paper, we solve the problem of committee review assignment with multi-aspect expertise matching by casting it as an integer linear programming problem. The proposed algorithm can naturally accommodate any probabilistic or deterministic method for modeling multiple aspects to automate committee review assignments. Evaluation using an existing data set shows that the proposed algorithm is effective for committee review assignments based on multi-aspect expertise matching.