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Deep variational models for collaborative filtering-based recommender systems

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Bibliographic Details
Main Authors: Jesús, Bobadilla, Fernando, Ortega, Abraham, Gutiérrez
Format: Book
Language:English
Published: Springer 2023
Subjects:
collaborative filtering
matrix factorization
Online Access:https://link.springer.com/article/10.1007/s00521-022-08088-2
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7372
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record_format dspace
spelling oai:localhost:PNK-73722023-03-31T03:41:09Z Deep variational models for collaborative filtering-based recommender systems Jesús, Bobadilla Fernando, Ortega Abraham, Gutiérrez collaborative filtering matrix factorization CC BY Deep learning provides accurate collaborative filtering models to improve recommender system results. Deep matrix factorization and their related collaborative neural networks are the state of the art in the field; nevertheless, both models lack the necessary stochasticity to create the robust, continuous, and structured latent spaces that variational autoencoders exhibit. On the other hand, data augmentation through variational autoencoder does not provide accurate results in the collaborative filtering field due to the high sparsity of recommender systems. 2023-03-31T03:41:09Z 2023-03-31T03:41:09Z 2023 Book https://link.springer.com/article/10.1007/s00521-022-08088-2 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7372 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic collaborative filtering
matrix factorization
spellingShingle collaborative filtering
matrix factorization
Jesús, Bobadilla
Fernando, Ortega
Abraham, Gutiérrez
Deep variational models for collaborative filtering-based recommender systems
description CC BY
format Book
author Jesús, Bobadilla
Fernando, Ortega
Abraham, Gutiérrez
author_facet Jesús, Bobadilla
Fernando, Ortega
Abraham, Gutiérrez
author_sort Jesús, Bobadilla
title Deep variational models for collaborative filtering-based recommender systems
title_short Deep variational models for collaborative filtering-based recommender systems
title_full Deep variational models for collaborative filtering-based recommender systems
title_fullStr Deep variational models for collaborative filtering-based recommender systems
title_full_unstemmed Deep variational models for collaborative filtering-based recommender systems
title_sort deep variational models for collaborative filtering-based recommender systems
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s00521-022-08088-2
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7372
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score 8.891145

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