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

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...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1646; no. 1; pp. 12151 - 12157
Main Authors Luo, Jianyan, Xing, Xing, Zheng, Hang, Xin, Mindong, Jia, Zhichun
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.09.2020
Subjects
Algorithms
Deep learning
Feature extraction
Machine learning
Noise reduction
Physics
Recommender systems
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1646/1/012151