GRE: Hybrid recommendations for NSDL collections

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

doi:10.1145/1555400.1555511

GRE: Hybrid recommendations for NSDL collections

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

Department of Business Administration

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	JCDL'09 - Proceedings of the 2009 ACM/IEEE Joint Conference on Digital Libraries
Pages	457
Number of pages	1
DOIs	https://doi.org/10.1145/1555400.1555511
Publication status	Published - 2009
Event	2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09 - Austin, TX, United States Duration: 2009 Jun 15 → 2009 Jun 19

Publication series

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

Other

Other	2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09
Country	United States
City	Austin, TX
Period	09/6/15 → 09/6/19

All Science Journal Classification (ASJC) codes

Engineering(all)

Access to Document

10.1145/1555400.1555511

Fingerprint Dive into the research topics of 'GRE: Hybrid recommendations for NSDL collections'. Together they form a unique fingerprint.

Cite this

@inproceedings{304783049f6b4006a6eed032b73e1638,

title = "GRE: Hybrid recommendations for NSDL collections",

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.",

author = "Todd Will and Anand Srinivasan and Michael Bieber and Il Im and Vincent Oria and Wu, {Yi Fang Brook}",

year = "2009",

doi = "10.1145/1555400.1555511",

language = "English",

isbn = "9781605586977",

series = "Proceedings of the ACM/IEEE Joint Conference on Digital Libraries",

pages = "457",

booktitle = "JCDL'09 - Proceedings of the 2009 ACM/IEEE Joint Conference on Digital Libraries",

}

Will, T, Srinivasan, A, Bieber, M, Im, I, Oria, V & Wu, YFB 2009, GRE: Hybrid recommendations for NSDL collections. in JCDL'09 - Proceedings of the 2009 ACM/IEEE Joint Conference on Digital Libraries. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, pp. 457, 2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09, Austin, TX, United States, 09/6/15. https://doi.org/10.1145/1555400.1555511

GRE : Hybrid recommendations for NSDL collections. / Will, Todd; Srinivasan, Anand; Bieber, Michael; Im, Il; Oria, Vincent; Wu, Yi Fang Brook.

JCDL'09 - Proceedings of the 2009 ACM/IEEE Joint Conference on Digital Libraries. 2009. p. 457 (Proceedings of the ACM/IEEE Joint Conference on Digital Libraries).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - GRE

T2 - 2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09

AU - Will, Todd

AU - Srinivasan, Anand

AU - Bieber, Michael

AU - Im, Il

AU - Oria, Vincent

AU - Wu, Yi Fang Brook

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=70450275820&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70450275820&partnerID=8YFLogxK

U2 - 10.1145/1555400.1555511

DO - 10.1145/1555400.1555511

M3 - Conference contribution

AN - SCOPUS:70450275820

SN - 9781605586977

T3 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries

SP - 457

BT - JCDL'09 - Proceedings of the 2009 ACM/IEEE Joint Conference on Digital Libraries

Y2 - 15 June 2009 through 19 June 2009

ER -