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dc.contributor.author
Schickel-Zuber, Vincent
dc.contributor.author
Faltings, Boi
dc.date.accessioned
2022-07-27T08:55:32Z
dc.date.available
2017-06-10T10:23:44Z
dc.date.available
2022-07-27T08:55:32Z
dc.date.issued
2007
dc.identifier.isbn
978-1-59593-609-7
en_US
dc.identifier.other
10.1145/1281192.1281257
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/57644
dc.description.abstract
Ontologies are being successfully used to overcome semanticheterogeneity, and are becoming fundamental elements of the SemanticWeb. Recently, it has also been shown that ontologies can be used tobuild more accurate and more personalized recommendation systems byinferencing missing user's preferences. However, these systemsassume the existence of ontologies, without considering theirconstruction. With product catalogs changing continuously, newtechniques are required in order to build these ontologies in realtime, and autonomously from any expert intervention.This paper focuses on this problem and show that it is possible tolearn ontologies autonomously by using clustering algorithms. Results on the MovieLens and Jester data sets show that recommendersystem with learnt ontologies significantly outperform the classical recommendation approach.
en_US
dc.language.iso
en
en_US
dc.publisher
Association for Computing Machinery
en_US
dc.subject
Ontology
en_US
dc.subject
Recommendation System
en_US
dc.subject
Clustering
en_US
dc.title
Using Hierarchical Clustering for Learning the Ontologies used in Recommendation Systems
en_US
dc.type
Other Conference Item
ethz.book.title
KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
en_US
ethz.pages.start
599
en_US
ethz.pages.end
608
en_US
ethz.event
13th International Conference on Knowledge Discovery and Data Mining (KDD 2007)
en_US
ethz.event.location
San Jose, CA, USA
en_US
ethz.event.date
August 12-15, 2007
en_US
ethz.identifier.wos
ethz.publication.place
New York, NY, USA
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-06-10T10:23:55Z
ethz.source
ECIT
ethz.identifier.importid
imp59364feb1667518696
ethz.ecitpid
pub:92183
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2017-07-12T16:53:48Z
ethz.rosetta.lastUpdated
2017-07-12T16:53:48Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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