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dc.contributor.author
Sarabadani Tafreshi, Amir Esmaeil
dc.contributor.author
Sarabadani Tafreshi, Amirehsan
dc.contributor.author
Ralescu, Anca L.
dc.date.accessioned
2018-06-26T13:25:27Z
dc.date.available
2018-06-26T12:02:45Z
dc.date.available
2018-06-26T12:33:10Z
dc.date.available
2018-06-26T13:25:27Z
dc.date.issued
2018-03
dc.identifier.issn
0975-900X
dc.identifier.issn
0976-2191
dc.identifier.other
10.5121/ijaia.2018.9204
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/272406
dc.identifier.doi
10.3929/ethz-b-000272406
dc.description.abstract
Current research on recommendation systems focuses on optimization and evaluation of the quality of ranked recommended results. One of the most common approaches used in digital paper libraries to present and recommend relevant search results, is ranking the papers based on their features. However, feature utility or relevance varies greatly from highly relevant to less relevant, and redundant. Departing from the existing recommendation systems, in which all item features are considered to be equally important, this study presents the initial development of an approach to feature weighting with the goal of obtaining a novel recommendation method in which features which are more effective have a higher contribution/weight to the ranking process. Furthermore, it focuses on obtaining ranking of results returned by a query through a collaborative weighting procedure carried out by human users. The collaborative feature-weighting procedure is shown to be incremental, which in turn leads to an incremental approach to feature-based similarity evaluation. The obtained system is then evaluated using Normalized Discounted Cumulative Gain (NDCG) with respect to a crowd-sourced ranked results. Comparison between the performance of the proposed and Ranking SVM methods shows that the overall ranking accuracy of the proposed approach outperforms the ranking accuracy of Ranking SVM method.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
AIRCC Publishing Corporation
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Ranking
en_US
dc.subject
recommendation system
en_US
dc.subject
feature weighting
en_US
dc.subject
support vector machine
en_US
dc.title
Ranking Based on Collaborative Feature Weighting Applied to the Recommendation of Research Papers
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
International Journal of Artificial Intelligence & Applications
ethz.journal.volume
9
en_US
ethz.journal.issue
2
en_US
ethz.journal.abbreviated
IJAIA
ethz.pages.start
47
en_US
ethz.pages.end
53
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.publication.place
Chennai, Tamil Nadu
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02634 - Institut für Elektronik / Institute for Electronics
en_US
ethz.leitzahl
ETH Zürich
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02634 - Institut für Elektronik / Institute for Electronics::03388 - Tröster, Gerhard (emeritus)
ethz.date.deposited
2018-06-26T12:02:47Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-06-26T12:33:13Z
ethz.rosetta.lastUpdated
2019-01-02T13:17:26Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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