Layered evaluation for data discovery and recommendation systems: An initial set of principles

dc.creator	Manouselis, N.	en
dc.creator	Karagiannidis, C.	en
dc.creator	Sampson, D. G.	en
dc.date.accessioned	2015-11-23T10:38:46Z
dc.date.available	2015-11-23T10:38:46Z
dc.date.issued	2014
dc.identifier	10.1109/ICALT.2014.152
dc.identifier.isbn	9781479940387
dc.identifier.uri	http://hdl.handle.net/11615/30661
dc.description.abstract	This paper examines how a layered evaluation framework proposed for adaptive systems (AS) can be applied in the case of recommender systems (RecSys). Our analysis indicates that implementing a layered-based evaluation has the potential to facilitate a more detailed and informed evaluation of RecSys, allowing researchers and developers to better understand how to improve them. © 2014 IEEE.	en
dc.source.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-84910069393&partnerID=40&md5=c46a2d771e48549c688f6fcce79e18f2
dc.subject	adaptive systems	en
dc.subject	layered evaluation	en
dc.subject	recommender systems	en
dc.subject	Data discovery	en
dc.title	Layered evaluation for data discovery and recommendation systems: An initial set of principles	en
dc.type	conferenceItem	en

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