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dc.contributor.author
Kalloori, Saikishore
dc.contributor.editor
Natarajan, Sriraam
dc.contributor.editor
Bhattacharya, Indrajit
dc.contributor.editor
Singh, Richa
dc.contributor.editor
Kumar, Arun
dc.contributor.editor
Ranu, Sayan
dc.contributor.editor
Bali, Kalika
dc.contributor.editor
K., Abinaya
dc.date.accessioned
2024-02-14T14:57:19Z
dc.date.available
2024-02-05T10:04:42Z
dc.date.available
2024-02-14T14:57:19Z
dc.date.issued
2024-01
dc.identifier.isbn
979-8-4007-1634-8
en_US
dc.identifier.other
10.1145/3632410.3632497
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/657628
dc.description.abstract
With the increasing desire to protect the users' data and provide data privacy, few recent research works have been focusing on federated learning to avoid centralized training for various machine learning problems. Federated learning involving clients such as mobile devices has been widely explored and there is little exploration done toward training a federated model involving corporate companies or organizations. In this work, we want to understand the capability of a federated recommendation model using three news publishers' users' data to generate cold-start recommendations. We train a recommendation model using federated learning from three news publishers and evaluate the cold-start recommendation performance of our federated model against a model trained with each news publisher's data alone. Our results demonstrate that federated learning boosts the cold start recommendation for all news publishers and there is a higher ranking performance when federated with news publishers that have similar user behavior.
en_US
dc.language.iso
en
en_US
dc.publisher
Association for Computing Machinery
en_US
dc.title
Federated Learning for Cold Start Recommendations
en_US
dc.type
Conference Paper
dc.date.published
2024-01-04
ethz.book.title
CODS-COMAD '24: Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)
en_US
ethz.pages.start
599
en_US
ethz.pages.end
600
en_US
ethz.event
7th Joint International Conference on Data Science & Management of Data (CODS-COMAD 2024)
en_US
ethz.event.location
Bangalore, India
en_US
ethz.event.date
January 4 - 7, 2024
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2024-02-05T10:04:43Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2024-02-14T14:57:21Z
ethz.rosetta.lastUpdated
2024-02-14T14:57:21Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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