@inproceedings{5b56b8b545ff4b0a9485cf0ae3fcc6c1,
title = "Time-aware and topic-based reviewer assignment",
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{\textquoteright} 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{\textquoteright} 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{\textquoteright} present reviews. Extensive experiments on a real-world dataset demonstrate the effectiveness of the proposed method.",
keywords = "Expert retrieval, Reviewer assignment, Time aware, Topic model",
author = "Hongwei Peng and Haojie Hu and Keqiang Wang and Xiaoling Wang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; International Workshops on Database Systems for Advanced Applications, DASFAA 2017, 4th International Workshop on Big Data Management and Service, BDMS 2017, 2nd Workshop on Big Data Quality Management, BDQM 2017, 4th International Workshop on Semantic Computing and Personalization, SeCoP 2017, 1st International Workshop on Data Management and Mining on MOOCs, DMMOOC 2017 ; Conference date: 27-03-2017 Through 30-03-2017",
year = "2017",
doi = "10.1007/978-3-319-55705-2\_11",
language = "英语",
isbn = "9783319557045",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "145--157",
editor = "Lijun Chang and Goce Trajcevski and Wen Hua and Zhifeng Bao",
booktitle = "Database Systems for Advanced Applications - DASFAA 2017 International Workshops",
address = "德国",
}