Can't see the forest for the trees? A citation recommendation system

Cornelia Caragea, Adrian Silvescu, Prasenjit Mitra, C. Lee Giles

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

32 Scopus citations

Abstract

Scientists continue to find challenges in the ever increasing amount of information that has been produced on a world wide scale, during the last decades. When writing a paper, an author searches for the most relevant citations that started or were the foundation of a particular topic, which would very likely explain the thinking or algorithms that are employed. The search is usually done using specific keywords submitted to literature search engines such as Google Scholar and CiteSeer. However, finding relevant citations is distinctive from producing articles that are only topically similar to an author's proposal. In this paper, we address the problem of citation recommendation using a singular value decomposition approach. The models are trained and evaluated on the Citeseer digital library. The results of our experiments show that the proposed approach achieves significant success when compared with collaborative filtering methods on the citation recommendation task.

Original languageEnglish (US)
Title of host publicationJCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries
Pages111-114
Number of pages4
DOIs
StatePublished - Aug 23 2013
Event13th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2013 - Indianapolis, IN, United States
Duration: Jul 22 2013Jul 26 2013

Publication series

NameProceedings of the ACM/IEEE Joint Conference on Digital Libraries
ISSN (Print)1552-5996

Other

Other13th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2013
CountryUnited States
CityIndianapolis, IN
Period7/22/137/26/13

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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