Name
Abstract | ||
|---|---|---|
The assessment of the academic papers submitted to scientific journals and conferences is mostly based on peer reviewing. The reviewers assigned to each paper are usually shortlisted from a program committee by matching paper topics with reviewers' expertise. However, reviewer assignments often need to be reconsidered at a later time, because reviewers can be temporarily unavailable or some of the assignments turn out to be critical due to the presence of conflicts. Therefore, there is a need for automated tools that support experts in seeking additional reviewers external to the program committee. In this paper, we propose an association rule-based methodology to recommend additional external reviewers. Weighted Association Rules (WARs), which represent strong associations between paper co-authors, are first extracted from a benchmark publication dataset. Then, they are exploited to discover potential conflicts of interest between co-reviewers as well as between reviewers and authors. Finally, reviewer assignments for papers with conflicts or with low-quality assignments are reconsidered. To tackle this issue, a subset of selected WARs provide promptly usable additional reviewer recommendations, as they highlight external experts who have profitably collaborated with the shortlisted reviewers. The effectiveness of the proposed methodology were validated on both journal and conference datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TETC.2018.2861214 | IEEE Transactions on Emerging Topics in Computing |
Keywords | DocType | Volume |
Additional reviewer assignment,conflict of interest detection,weighted association rule mining | Journal | 9 |
Issue | ISSN | Citations |
1 | 2168-6750 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Cagliero | 1 | 285 | 31.63 |
| Paolo Garza | 2 | 426 | 39.13 |
| Andrea Pasini | 3 | 0 | 0.34 |
| Elena Baralis | 4 | 1319 | 186.33 |