GRE: Hybrid recommendations for NSDL collections

Todd Will, Anand Srinivasan, Michael Bieber, Il Im, Vincent Oria, Yi Fang Brook Wu

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

1 Citation (Scopus)

Abstract

Recommendation systems have been proven to reduce the time and effort required by users to find relevant items, but there are only sporadic reports on their application in digital libraries. The General Recommendation Engine (GRE) is composed of the text search system Lucene augmented by the well-understood content based and collaborative filtering techniques and the first application of knowledge based recommendation in digital libraries to recommend items from 22 National Science Digital Library collections. In this study comprised of 60 subjects, the GRE statistically outperformed the baseline system Lucene in all areas of evaluation.

Original languageEnglish
Title of host publicationJCDL'09 - Proceedings of the 2009 ACM/IEEE Joint Conference on Digital Libraries
Pages457
Number of pages1
DOIs
Publication statusPublished - 2009
Event2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09 - Austin, TX, United States
Duration: 2009 Jun 152009 Jun 19

Publication series

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

Other

Other2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09
CountryUnited States
CityAustin, TX
Period09/6/1509/6/19

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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