SubSift: a novel application of the vector space model to support the academic peer review process

S Price, PA Flach, S Spiegler

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

SubSift matches submitted conference or journal papers to potential peer reviewers based on the similarity between the paper's abstract and the reviewer's publications as found in online bibliographic databases such as Google Scholar. Using concepts from information retrieval including a bag-of-words representation and cosine similarity, the SubSift tools were originally created to streamline the peer review process for the ACM SIGKDD'09 data mining conference. This paper describes how these tools were subsequently developed and deployed in the form of web services designed to support not only peer review but also personalised data discovery and mashups. SubSift has already been used by several major data mining conferences and interesting applications in other fields are now emerging.
Translated title of the contributionSubSift: a novel application of the vector space model to support the academic peer review process
Original languageEnglish
Title of host publicationWorkshop on Applications of Pattern Analysis (WAPA 2010)
Pages20 - 27
Volume11
Publication statusPublished - Sep 2010

Bibliographical note

Editors: Tom Diethe, Nello Cristianini, and John Shawe-Taylor
Publisher: Journal of Machine Learning Research
Name and Venue of Conference: Workshop on Applications of Pattern Analysis (WAPA 2010)
Name and Venue of Event: Windsor, UK
Conference Organiser: University College London
Other: http://www.pascal-network.org/wapa2010

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