TY - GEN
T1 - Constrained multi-aspect expertise matching for committee review assignment
AU - Karimzadehgan, Maryam
AU - Zhai, Cheng Xiang
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Algorithms
KW - Combinatorial optimization
KW - Review assignment
KW - Topic models
UR - http://www.scopus.com/inward/record.url?scp=74549143727&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=74549143727&partnerID=8YFLogxK
U2 - 10.1145/1645953.1646207
DO - 10.1145/1645953.1646207
M3 - Conference contribution
AN - SCOPUS:74549143727
SN - 9781605585123
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1697
EP - 1700
BT - ACM 18th International Conference on Information and Knowledge Management, CIKM 2009
T2 - ACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Y2 - 2 November 2009 through 6 November 2009
ER -