Variational autoencoder with side information in recommendation systems

doi:10.16511/j.cnki.qhdxxb.2018.21.016

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Abstract The variational autoencoder (VAE) unsupervised learning method can provide excellent results in recommendation systems. Recommendation systems seek to accurately identify a missing value with the VAE learning a latent factor from the input and then predicting when to use this for reconstructing the result. Side information was added to the VAE to improve the predictions with tests on datasets including MovieLens and grades data showing that it can significantly improve the prediction accuracy by up to 31% with enough side information with the grades dataset.

Keywords recommendation systems variational inference autoencoder collaborative filtering

Issue Date: 15 August 2018

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	LIU Weidong
	LIU Yaning

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LIU Weidong,LIU Yaning. Variational autoencoder with side information in recommendation systems[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(8): 698-702.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2018.21.016 OR http://jst.tsinghuajournals.com/EN/Y2018/V58/I8/698

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