Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Advances in Database Technology - EDBT 2016 |
| Untertitel | 19th International Conference on Extending Database Technology, Proceedings |
| Herausgeber/-innen | Ioana Manolescu, Evaggelia Pitoura, Amelie Marian, Sofian Maabout, Letizia Tanca, Georgia Koutrika, Kostas Stefanidis |
| Seiten | 712-713 |
| Seitenumfang | 2 |
| ISBN (elektronisch) | 9783893180707 |
| Publikationsstatus | Veröffentlicht - 2016 |
| Veranstaltung | 19th International Conference on Extending Database Technology, EDBT 2016 - Bordeaux, Frankreich Dauer: 15 März 2016 → 18 März 2016 |
Publikationsreihe
| Name | Advances in Database Technology - EDBT |
|---|---|
| Band | 2016-March |
| ISSN (elektronisch) | 2367-2005 |
Abstract
In this work, we address the problem of contextual recommendations by exploiting the concept of subspace clustering. Specifically, we pre-partition users that have rated subsets of data items similarly into clusters and we associate a context situation with each cluster. The cluster context is defined as any internally stored information that can be used to characterize the cluster members per se. Then, given a query context, we identify the clusters with the most similar context, and we use their members for making suggestions in a collaborative filtering manner.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Informatik (insg.)
- Software
- Informatik (insg.)
- Angewandte Informatik
Zitieren
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- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Advances in Database Technology - EDBT 2016: 19th International Conference on Extending Database Technology, Proceedings. Hrsg. / Ioana Manolescu; Evaggelia Pitoura; Amelie Marian; Sofian Maabout; Letizia Tanca; Georgia Koutrika; Kostas Stefanidis. 2016. S. 712-713 (Advances in Database Technology - EDBT; Band 2016-March).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Cluster-based contextual recommendations
AU - Stefanidis, Kostas
AU - Ntoutsi, Eirini
PY - 2016
Y1 - 2016
N2 - In this work, we address the problem of contextual recommendations by exploiting the concept of subspace clustering. Specifically, we pre-partition users that have rated subsets of data items similarly into clusters and we associate a context situation with each cluster. The cluster context is defined as any internally stored information that can be used to characterize the cluster members per se. Then, given a query context, we identify the clusters with the most similar context, and we use their members for making suggestions in a collaborative filtering manner.
AB - In this work, we address the problem of contextual recommendations by exploiting the concept of subspace clustering. Specifically, we pre-partition users that have rated subsets of data items similarly into clusters and we associate a context situation with each cluster. The cluster context is defined as any internally stored information that can be used to characterize the cluster members per se. Then, given a query context, we identify the clusters with the most similar context, and we use their members for making suggestions in a collaborative filtering manner.
UR - http://www.scopus.com/inward/record.url?scp=85046691231&partnerID=8YFLogxK
U2 - 10.5441/002/edbt.2016.100
DO - 10.5441/002/edbt.2016.100
M3 - Conference contribution
AN - SCOPUS:85046691231
T3 - Advances in Database Technology - EDBT
SP - 712
EP - 713
BT - Advances in Database Technology - EDBT 2016
A2 - Manolescu, Ioana
A2 - Pitoura, Evaggelia
A2 - Marian, Amelie
A2 - Maabout, Sofian
A2 - Tanca, Letizia
A2 - Koutrika, Georgia
A2 - Stefanidis, Kostas
T2 - 19th International Conference on Extending Database Technology, EDBT 2016
Y2 - 15 March 2016 through 18 March 2016
ER -