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 |
| Number of pages | 1 |
| DOIs | |
| Publication status | Published - 2009 Nov 30 |
| Event | 2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09 - Austin, TX, United States Duration: 2009 Jun 15 → 2009 Jun 19 |
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 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
}
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.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - GRE
T2 - Hybrid recommendations for NSDL collections
AU - Will, Todd
AU - Srinivasan, Anand
AU - Bieber, Michael
AU - Im, Il
AU - Oria, Vincent
AU - Wu, Yi Fang Brook
PY - 2009/11/30
Y1 - 2009/11/30
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
BT - JCDL'09 - Proceedings of the 2009 ACM/IEEE Joint Conference on Digital Libraries
ER -