Abstract
Peer review has become the most widely-used mechanism to judge the quality of submitted papers at academic conferences or journals. However, a challenging task in peer review is to assign papers to appropriate reviewers. Both the research directions of reviewers and topics of submitted papers are often multifaceted. Besides, reviewers’ research direction may change over time and their published papers closer to current time reflect their current research direction better. Hence in this paper, we present a time-aware and topic-based reviewer assignment model. We first crawl papers published by reviewers over years from web, and then build a time-aware reviewers’ personal profile using topic model to represent the expertise of reviewers. Then the relevant degree between reviewer and submitted paper is calculated through the similarity measure. In addition, by considering statistical characteristics such as TF-IDF of the papers, the matching degree between reviewer and submitted paper is further improved. At the same time, we also consider the quality of all past reviews to measure the reviewers’ present reviews. Extensive experiments on a real-world dataset demonstrate the effectiveness of the proposed method.
Keywords
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Acknowledgments
This work was supported by NSFC grants (No. 61472141 and 61532021), Shanghai Knowledge Service Platform Project (No. ZF1213), Shanghai Leading Academic Discipline Project (Project Number B412), and Shanghai Agriculture Applied Technology Development Program (Grant No. G20160201).
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Peng, H., Hu, H., Wang, K., Wang, X. (2017). Time-Aware and Topic-Based Reviewer Assignment. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10179. Springer, Cham. https://doi.org/10.1007/978-3-319-55705-2_11
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DOI: https://doi.org/10.1007/978-3-319-55705-2_11
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