Deep Learning Architecture for Collaborative Filtering Recommender Systems

This paper provides an innovative deep learning architecture to improve collaborative filtering results in recommender systems. It exploits the potential of the reliability concept to raise predictions and recommendations quality by incorporating prediction errors (reliabilities) in the deep learnin...

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Hlavní autoři:	Jesus Bobadilla, Santiago Alonso, Antonio Hernando
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2020-04-01
Edice:	Applied Sciences
Témata:	collaborative filtering reliabilities deep learning recommender systems matrix factorization
On-line přístup:	https://www.mdpi.com/2076-3417/10/7/2441
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