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

Simon Price, Peter A. Flach, Sebastian Spiegler
Proceedings of the First Workshop on Applications of Pattern Analysis, PMLR 11:20-27, 2010.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v11-price10a, title = {SubSift: a novel application of the vector space model to support the academic research process}, author = {Price, Simon and Flach, Peter A. and Spiegler, Sebastian}, booktitle = {Proceedings of the First Workshop on Applications of Pattern Analysis}, pages = {20--27}, year = {2010}, editor = {Diethe, Tom and Cristianini, Nello and Shawe-Taylor, John}, volume = {11}, series = {Proceedings of Machine Learning Research}, address = {Cumberland Lodge, Windsor, UK}, month = {01--03 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v11/price10a/price10a.pdf}, url = {https://proceedings.mlr.press/v11/price10a.html}, 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.} }
Endnote
%0 Conference Paper %T SubSift: a novel application of the vector space model to support the academic research process %A Simon Price %A Peter A. Flach %A Sebastian Spiegler %B Proceedings of the First Workshop on Applications of Pattern Analysis %C Proceedings of Machine Learning Research %D 2010 %E Tom Diethe %E Nello Cristianini %E John Shawe-Taylor %F pmlr-v11-price10a %I PMLR %P 20--27 %U https://proceedings.mlr.press/v11/price10a.html %V 11 %X 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.
RIS
TY - CPAPER TI - SubSift: a novel application of the vector space model to support the academic research process AU - Simon Price AU - Peter A. Flach AU - Sebastian Spiegler BT - Proceedings of the First Workshop on Applications of Pattern Analysis DA - 2010/09/30 ED - Tom Diethe ED - Nello Cristianini ED - John Shawe-Taylor ID - pmlr-v11-price10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 11 SP - 20 EP - 27 L1 - http://proceedings.mlr.press/v11/price10a/price10a.pdf UR - https://proceedings.mlr.press/v11/price10a.html AB - 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. ER -
APA
Price, S., Flach, P.A. & Spiegler, S.. (2010). SubSift: a novel application of the vector space model to support the academic research process. Proceedings of the First Workshop on Applications of Pattern Analysis, in Proceedings of Machine Learning Research 11:20-27 Available from https://proceedings.mlr.press/v11/price10a.html.

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