Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Database Technology - EDBT 2016 |
| Subtitle of host publication | 19th International Conference on Extending Database Technology, Proceedings |
| Editors | Ioana Manolescu, Evaggelia Pitoura, Amelie Marian, Sofian Maabout, Letizia Tanca, Georgia Koutrika, Kostas Stefanidis |
| Pages | 712-713 |
| Number of pages | 2 |
| ISBN (electronic) | 9783893180707 |
| Publication status | Published - 2016 |
| Event | 19th International Conference on Extending Database Technology, EDBT 2016 - Bordeaux, France Duration: 15 Mar 2016 → 18 Mar 2016 |
Publication series
| Name | Advances in Database Technology - EDBT |
|---|---|
| Volume | 2016-March |
| ISSN (electronic) | 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 subject areas
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Science Applications
Cite this
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Advances in Database Technology - EDBT 2016: 19th International Conference on Extending Database Technology, Proceedings. ed. / Ioana Manolescu; Evaggelia Pitoura; Amelie Marian; Sofian Maabout; Letizia Tanca; Georgia Koutrika; Kostas Stefanidis. 2016. p. 712-713 (Advances in Database Technology - EDBT; Vol. 2016-March).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 -