HIGHLIGHTS
- who: Cesare Bernardis from the DEIB, Politecnico di Milano, Milan, Italy have published the paper: NFC: a deep and hybrid item-based model for item cold-start recommendation, in the Journal: (JOURNAL)
- what: The authors propose Neural Feature Combiner (NFC) a novel deep learning item-based approach for cold-start item recommendation. The authors compare NFC with three variants of the same model that learn from user feedback showing the advantages of learning from similarities in terms of accuracy and convergence time. With an extensive set of experiments on four datasets the authors show that . . .
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