The Recommendation Algorithm Based on Improved Conditional Variational Autoencoder and Constrained Probabilistic Matrix Factorization

An improved recommendation algorithm based on Conditional Variational Autoencoder (CVAE) and Constrained Probabilistic Matrix Factorization (CPMF) is proposed to address the issues of poor recommendation performance in traditional user-based collaborative filtering algorithms caused by data sparsity...

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Vydáno v:	Applied sciences Ročník 13; číslo 21; s. 12027
Hlavní autoři:	Zhang, Yunfei, Xu, Hongzhen, Yu, Xiaojun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.11.2023
Témata:	Algorithms Analysis auxiliary information Collaboration collaborative filtering conditional variational autoencoder constrained probabilistic matrix factorization feature matric Neural networks Optimization techniques Ratings & rankings Recommender systems Sparsity User behavior New York
ISSN:	2076-3417, 2076-3417
On-line přístup:	Získat plný text
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