Reviewer profiling using sparse matrix regression

Evangelos E. Papalexakis, Nicholas D. Sidiropoulos, Minos N. Garofalakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


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 keywords for appropriate specificity. Exploiting this sparsity, we propose a sparse matrix factorization approach in lieu of classical SVD-based LSI or NMF-type approaches. We illustrate the merits of our approach using real conference data, and expert scoring of the assignments by a seasoned TPC chair in the area.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
Number of pages6
StatePublished - 2010
Externally publishedYes
Event10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - Sydney, NSW, Australia
Duration: Dec 14 2010Dec 17 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
CitySydney, NSW


  • Lasso
  • Latent semantic indexing
  • Non-negative matrix factorization
  • Reviewer profiling
  • Singular value decomposition
  • Sparse regression
  • Text mining


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