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Inf. Syst."],"published-print":{"date-parts":[[2020,1,31]]},"abstract":"<jats:p>\n            The paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. It aims to recommend appropriate experts in a discipline to comment on the quality of papers of others in that discipline. How to effectively and accurately recommend reviewers for the submitted papers is a meaningful and still tough task. Generally, the relationship between a paper and a reviewer often depends on the semantic expressions of them. Creating a more expressive representation can make the peer-review process more robust and less arbitrary. So the representations of a paper and a reviewer are very important for the paper-reviewer recommendation. Actually, a reviewer or a paper often belongs to multiple research fields, which increases difficulty in paper-reviewer recommendation. In this article, we propose a Multi-Label Classification method using a HIErarchical and transPArent Representation named\n            <jats:italic>Hiepar-MLC<\/jats:italic>\n            . First, we introduce HIErarchical and transPArent Representation (Hiepar) to express the semantic information of the reviewer and the paper. Hiepar is learned from a two-level bidirectional gated recurrent unit based network applying the attention mechanism. It is capable of capturing the two-level hierarchical information (word-sentence-document) and highlighting the elements in reviewers or papers to support the labels. This word-sentence-document information mirrors the hierarchical structure of a reviewer or a paper and captures the exact semantics of them. Then we transform the paper-reviewer recommendation problem into a multi-level classification issue, whose multiple research labels exactly guide the learning process. It is flexible in that we can select any multi-label classification method to solve the paper-reviewer recommendation problem. Further, we propose a simple multi-label-based reviewer assignment (MLBRA) strategy to select the appropriate reviewers. It is interesting in that we also explore the paper-reviewer recommendation in the coarse-grain granularity. Extensive experiments on the real-world dataset consisting of the papers in the ACM Digital Library show that Hiepar-MLC achieves better label prediction performance than the existing representation alternatives. In addition, with the MLBRA strategy, we show the effectiveness and the feasibility of our transformation from paper-reviewer recommendation to multi-label classification.\n          <\/jats:p>","DOI":"10.1145\/3361719","type":"journal-article","created":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T09:07:52Z","timestamp":1580720872000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["A Multi-Label Classification Method Using a Hierarchical and Transparent Representation for Paper-Reviewer Recommendation"],"prefix":"10.1145","volume":"38","author":[{"given":"Dong","family":"Zhang","sequence":"first","affiliation":[{"name":"Anhui University, Hefei, China"}]},{"given":"Shu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}]},{"given":"Zhen","family":"Duan","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}]},{"given":"Yanping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}]},{"given":"Jie","family":"Tang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,2,3]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. 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An intelligent decision support approach for reviewer assignment in R8D project selection. Computers in Industry 76 C (2016) 1--10.  Ou Liu Jun Wang Jian Ma and Yonghong Sun. 2016. An intelligent decision support approach for reviewer assignment in R8D project selection. Computers in Industry 76 C (2016) 1--10.","DOI":"10.1016\/j.compind.2015.11.001"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1089815.1089821"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2645710.2645749"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.01.022"},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the Annual Meeting of the Association for Computational Linguistics. 694--707","author":"Tan B. Xiang M."},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the International Conference on Machine Learning. 957--966","author":"Kusner Matt J."},{"key":"e_1_2_1_30_1","unstructured":"Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv:1301.3781.  Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv:1301.3781."},{"key":"e_1_2_1_31_1","volume-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 500--509","author":"David"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44851-9_28"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2723727"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2016.2520371"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459199"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55705-2_11"},{"key":"e_1_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Simon Price and Peter A. Flach. 2017. Computational support for academic peer review: A perspective from artificial intelligence.Communications of the ACM 60 3 (2017) 70--79.  Simon Price and Peter A. Flach. 2017. 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