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 Scopus citations

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 (US)
Title of host publicationJCDL'09 - Proceedings of the 2009 ACM/IEEE Joint Conference on Digital Libraries
Number of pages1
DOIs
StatePublished - Nov 30 2009
Event2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09 - Austin, TX, United States
Duration: Jun 15 2009Jun 19 2009

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
Country/TerritoryUnited States
CityAustin, TX
Period6/15/096/19/09

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • Collaborative filtering
  • Content based
  • Digital libraries
  • Knowledge based recommendation
  • Recommendation systems
  • Text search engine
  • User interface

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