File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Weighted coverage based reviewer assignment

TitleWeighted coverage based reviewer assignment
Authors
KeywordsPaper reviewer assignment
Group coverage
Stage deepening greedy
Issue Date2015
PublisherACM Press.
Citation
The 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD '15), Melbourne, Australia, 22-27 June 2015. In Conference Proceedings, 2015, p. 2031-2046 How to Cite?
AbstractPeer reviewing is a standard process for assessing the quality of submissions at academic conferences and journals. A very important task in this process is the assignment of reviewers to papers. However, achieving an appropriate assignment is not easy, because all reviewers should have similar load and the subjects of the assigned papers should be consistent with the reviewers' expertise. In this paper, we propose a generalized framework for fair reviewer assignment. We first extract the domain knowledge from the reviewers' published papers and model this knowledge as a set of topics. Then, we perform a group assignment of reviewers to papers, which is a generalization of the classic Reviewer Assignment Problem (RAP), considering the relevance of the papers to topics as weights. We study a special case of the problem, where reviewers are to be found for just one paper (Journal Assignment Problem) and propose an exact algorithm which is fast in practice, as opposed to brute-force solutions. For the general case of having to assign multiple papers, which is too hard to be solved exactly, we propose a greedy algorithm that achieves a 1/2-approximation ratio compared to the exact solution. This is a great improvement compared to the 1/3-approximation solution proposed in previous work for the simpler coverage-based reviewer assignment problem, where there are no weights on topics. We theoretically prove the approximation bound of our solution and experimentally show that it is superior to the current state-of-the-art.
Persistent Identifierhttp://hdl.handle.net/10722/213625
ISBN

 

DC FieldValueLanguage
dc.contributor.authorKou, NM-
dc.contributor.authorU, LH-
dc.contributor.authorMamoulis, N-
dc.contributor.authorGong, Z-
dc.date.accessioned2015-08-07T04:29:34Z-
dc.date.available2015-08-07T04:29:34Z-
dc.date.issued2015-
dc.identifier.citationThe 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD '15), Melbourne, Australia, 22-27 June 2015. In Conference Proceedings, 2015, p. 2031-2046-
dc.identifier.isbn978-1-4503-2758-9-
dc.identifier.urihttp://hdl.handle.net/10722/213625-
dc.description.abstractPeer reviewing is a standard process for assessing the quality of submissions at academic conferences and journals. A very important task in this process is the assignment of reviewers to papers. However, achieving an appropriate assignment is not easy, because all reviewers should have similar load and the subjects of the assigned papers should be consistent with the reviewers' expertise. In this paper, we propose a generalized framework for fair reviewer assignment. We first extract the domain knowledge from the reviewers' published papers and model this knowledge as a set of topics. Then, we perform a group assignment of reviewers to papers, which is a generalization of the classic Reviewer Assignment Problem (RAP), considering the relevance of the papers to topics as weights. We study a special case of the problem, where reviewers are to be found for just one paper (Journal Assignment Problem) and propose an exact algorithm which is fast in practice, as opposed to brute-force solutions. For the general case of having to assign multiple papers, which is too hard to be solved exactly, we propose a greedy algorithm that achieves a 1/2-approximation ratio compared to the exact solution. This is a great improvement compared to the 1/3-approximation solution proposed in previous work for the simpler coverage-based reviewer assignment problem, where there are no weights on topics. We theoretically prove the approximation bound of our solution and experimentally show that it is superior to the current state-of-the-art.-
dc.languageeng-
dc.publisherACM Press.-
dc.relation.ispartofProceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD '15-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectPaper reviewer assignment-
dc.subjectGroup coverage-
dc.subjectStage deepening greedy-
dc.titleWeighted coverage based reviewer assignment-
dc.typeConference_Paper-
dc.identifier.emailMamoulis, N: nikos@cs.hku.hk-
dc.identifier.authorityMamoulis, N=rp00155-
dc.description.naturepostprint-
dc.identifier.doi10.1145/2723372.2723727-
dc.identifier.hkuros246264-
dc.identifier.spage2031-
dc.identifier.epage2046-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats