Reviewer Profiling Using Sparse Matrix Regression

Thousands of scientific conferences happen every year, and each involves a laborious scientific peer review process conducted by one or more busy scientists serving as Technical/Scientific Program Committee (TPC) chair(s). The chair(s) must match submitted papers to their reviewer pool in such a way that i) each paper is reviewed by experts in its subject matter; and ii) no reviewer is overloaded with reviews or under-utilized. Towards this end, seasoned TPC chairs know the value of reviewer and paper profiling: summarizing the expertise / interests of each reviewer and the subject matter of each paper using judiciously chosen domain-specific keywords. An automated profiling algorithm is proposed for this purpose, which starts from generic / noisy reviewer profiles extracted using Google Scholar and derives custom conference-centric reviewer and paper profiles. Each reviewer is expert on few sub-topics, whereas the pool of reviewers and the conference may collectively need many more ke...

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Evangelos E. Papalexakis, Nicholas D. Sidiropoulos, Minos N. Garofalakis
• Keywords: Data Mining, ICDM 2010, Matrix Factorization, Reviewer, Subject Matter