Deep Learning Architecture for Collaborative Filtering Recommender Systems

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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Bibliographic Details
Main Authors:	Jesus Bobadilla, Santiago Alonso, Antonio Hernando
Format:	Article
Language:	English
Published:	MDPI AG 2020-04-01
Series:	Applied Sciences
Subjects:	collaborative filtering reliabilities deep learning recommender systems matrix factorization
Online Access:	https://www.mdpi.com/2076-3417/10/7/2441

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