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Reproducibility Analysis of Recommender Systems relying on ..:

, In: Proceedings of the 17th ACM Conference on Recommender Systems,

Lops, Pasquale ; Musacchio, Elio ; Musto, Cataldo... - p. 554-564 , 2023

Link: https://dl.acm.org/doi/10.1145/3604915.3609492

RT 
T1 <cTitle>Proceedings of the 17th ACM Conference on Recommender Systems</cTitle>: T1 <Title>Reproducibility Analysis of Recommender Systems relying on Visual Features: traps, pitfalls, and countermeasures</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3609492&Exemplar=1&LAN=DE
				
A1 Lops, Pasquale
A1 Musacchio, Elio
A1 Musto, Cataldo
A1 Polignano, Marco
A1 Silletti, Antonio
A1 Semeraro, Giovanni
PB ACM
YR 2023
K1 Neural Networks
K1 Recommendation Frameworks
K1 Recommender Systems
K1 Reproducibility
K1 Reproducibility Analysis
K1 Visual Features
K1 Information systems
K1 Information retrieval
K1 Retrieval tasks and goals
K1 Recommender systems
K1 Computing methodologies
K1 Machine learning
K1 Learning settings
K1 Learning from implicit feedback
K1 Information systems applications
K1 Data mining
K1 Collaborative filtering
K1 Document representation
K1 Content analysis and feature selection
SP 554
OP 564
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3604915.3609492
DO https://dl.acm.org/doi/10.1145/3604915.3609492
SF ELIB - SuUB Bremen

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