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

Simon Price, Peter A. Flach, Sebastian Spiegler, SubSift: a novel application of the vector space model to support the academic peer review process. Workshop on Applications of Pattern Analysis (WAPA 2010), Windsor, UK. ISSN 19387228, pp. 20–27. September 2010. PDF, 461 Kbytes.

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.

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