Multi-label Reviewer Profile Building and Ranking based on Expertise, Recency, Authority and h-index: Vital Module of Reviewer Paper Assignment
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Abstract
Reviewers serve as the key executors and prove to be the essential resources of the review process. The work aims to address and solve one of the problems associated with reviewer paper assignment problem that is identifying an expert with relevant knowledge of given research topic domain. We propose an LDA based model building for reviewer profiles with multiple domain labels using expert’s existing publications. Further reinforcement of ranking them within the specific research domain is done based on number of publications in topic domain, recency/freshness, and h-index. We provide test results for a dataset of 107 reviewers with their 900 publications collected from DBLP and demonstrate that average Precision for top@5 topics with reference to manual annotations is 81% for 10 topic LDA model.
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