SDAE-LFM: A Latent Factor Model for Recommendation Based on Stack Denoising AutoEncoder

Autor:	Hang Zheng, Jianyan Luo, Zhichun Jia, Xing Xing, Mindong Xin
Rok vydání:	2020
Předmět:	History Denoising autoencoder Stack (abstract data type) Computer science business.industry Factor (programming language) Pattern recognition Artificial intelligence business computer Computer Science Applications Education computer.programming_language
Zdroj:	Journal of Physics: Conference Series. 1646:012151
ISSN:	1742-6596 1742-6588
Popis:	Recommendation methods usually associated with data sparsity. The traditional recommendation methods take the users’ rating information as the recommendation basis, which ignore the latent features that can be taking into consideration to model for better recommendations. In order to deal with these problems, we proposed a latent factor model recommendation algorithm based on stack denoising autoencoder (SDAE-LFM), applying Deep Learning technology for latent feature representation learning. A stack denoising autoencoder is applied to extracting feature about item from the label information. Then we factorize the item feature information to perform matrix decomposition training. Finally, we predict the result by the user-item preference matrix. Experimental results on these datasets demonstrate that the proposed recommendation method has better performance.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b3662233e0f3bb7a3dafe7aa8b7a9b14 https://doi.org/10.1088/1742-6596/1646/1/012151 Zobrazit plný text záznamu