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清华大学学报(自然科学版)  2018, Vol. 58 Issue (8): 698-702    DOI: 10.16511/j.cnki.qhdxxb.2018.21.016
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
推荐系统中的带辅助信息的变分自编码器
刘卫东, 刘亚宁
清华大学 计算机科学与技术系, 北京 100084
Variational autoencoder with side information in recommendation systems
LIU Weidong, LIU Yaning
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
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摘要 变分自编码器是一种非常简洁有效的非监督学习方法,应用在推荐系统领域也能取得极佳的性能。推荐系统的主要工作之一是对缺失的数据进行估计并补全,变分自编码器通过对已有数据的学习和抽象能够挖掘出数据间隐式的关联因子,并基于此完成对缺失数据的预测。该文将额外的辅助信息加入到变分自编码器中以提高预测的准确度,并通过在包括高考成绩及电影评分等在内的实际数据集测试中验证了辅助信息的有效性,当辅助信息充足时在高考成绩数据集上最多可以降低31%的均方根误差。
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关键词 推荐系统变分推理自编码器协同过滤    
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.
Key wordsrecommendation systems    variational inference    autoencoder    collaborative filtering
收稿日期: 2018-02-07      出版日期: 2018-08-15
引用本文:   
刘卫东, 刘亚宁. 推荐系统中的带辅助信息的变分自编码器[J]. 清华大学学报(自然科学版), 2018, 58(8): 698-702.
LIU Weidong, LIU Yaning. Variational autoencoder with side information in recommendation systems. Journal of Tsinghua University(Science and Technology), 2018, 58(8): 698-702.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2018.21.016  或          http://jst.tsinghuajournals.com/CN/Y2018/V58/I8/698
  表1 不同模型在 MovieLens数据集上的 RMSE
  图1 不同模型在高考成绩数据集上的均方根误差
  表2 sVAE 在高考成绩数据集不同的辅助信息下的 RMSE
  图2 在高考成绩数据集上不同辅助信息 维度下的模型优化收敛时间
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