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1 Ergebnisse
1
FedCDR : Federated Cross-Domain Recommendation for Priva..:
, In:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management
,
Meihan, Wu
;
Li, Li
;
Tao, Chang
... - p. 2179-2188 , 2022
Link:
https://dl.acm.org/doi/10.1145/3511808.3557320
RT T1 <cTitle>Proceedings of the 31st ACM International Conference on Information & Knowledge Management</cTitle>: T1 <Title>FedCDR : Federated Cross-Domain Recommendation for Privacy-Preserving Rating Prediction</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3557320&Exemplar=1&LAN=DE A1 Meihan, Wu A1 Li, Li A1 Tao, Chang A1 Rigall, Eric A1 Xiaodong, Wang A1 Cheng-Zhong, Xu PB ACM YR 2022 K1 cold-start problem K1 cross-domain recommendation K1 personalized federated learning K1 rating prediction K1 Information systems K1 Information retrieval K1 Retrieval tasks and goals K1 Recommender systems K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks K1 Security and privacy K1 Human and societal aspects of security and privacy K1 Privacy protections SP 2179 OP 2188 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3511808.3557320 DO https://dl.acm.org/doi/10.1145/3511808.3557320 SF ELIB - SuUB Bremen
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