推荐系统中的带辅助信息的变分自编码器

刘卫东, 刘亚宁

清华大学学报（自然科学版） ›› 2018, Vol. 58 ›› Issue (8) : 698-702.

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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

推荐系统中的带辅助信息的变分自编码器

  • 刘卫东, 刘亚宁
作者信息 +

Variational autoencoder with side information in recommendation systems

  • LIU Weidong, LIU Yaning
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文章历史 +

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。

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.

关键词

推荐系统 / 变分推理 / 自编码器 / 协同过滤

Key words

recommendation systems / variational inference / autoencoder / collaborative filtering

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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 https://doi.org/10.16511/j.cnki.qhdxxb.2018.21.016
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 https://doi.org/10.16511/j.cnki.qhdxxb.2018.21.016

参考文献

[1] NELWAMONDO F V, MOHAMED S, MARWALA T. Missing data:A comparison of neural network and expectation maximisation techniques[J]. Current Science, 2007, 93(11):1514-1521.
[2] RESNICK P, VARIAN H R. Recommender systems[J]. Communications of the ACM, 1997, 40(3):56-58.
[3] BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[J]. Uncertainty in Artificial Intelligence, 1998, 98(7):43-52.
[4] HERLOCKER J L, KONSTAN J A, TERVEEN L G, et al. Evaluating collaborative filtering recommender systems[J]. ACM Transactions on Information Systems (TOIS), 2004, 22(1):5-53.
[5] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web. New York:ACM, 2001:285-295.
[6] KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8):30-37.
[7] SALAKHUTDINOV R, MNIH A, HINTON G. Restricted Boltzmann machines for collaborative filtering[C]//Proceedings of the 24th International Conference on Machine Learning. New York:ACM, 2007:791-798.
[8] STRUB F, GAUDEL R, MARY J. Hybrid recommender system based on autoencoders[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. New York:ACM, 2016:11-16.
[9] OUYANG Y, LIU W, RONG W, et al. Autoencoder-based collaborative filtering[C]//Proceedings of the 21st International Conference on Neural Information Processing. Berlin:Springer, 2014:284-291.
[10] SEDHAIN S, MENON A K, SANNER S, et al. Autorec:Autoencoders meet collaborative filtering[C]//Proceedings of the 24th International Conference on World Wide Web. New York:ACM, 2015:111-112.
[11] SUZUKI Y, OZAKI T. Stacked denoising autoencoder-based deep collaborative filtering using the change of similarity[C]//Proceedings of the 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA). Piscataway:IEEE, 2017:498-502.
[12] LI X P, SHE J. Collaborative variational autoencoder for recommender systems[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2017:305-314.
[13] BRAND M. Incremental singular value decomposition of uncertain data with missing values[C]//Proceedings of the 7th European Conference on Computer Vision. Berlin:Springer, 2002:707-720.
[14] SØNDERBY C K, RAIKO T, MAALØE L, et al. Ladder variational autoencoders[C]//Proceedings of the 29th Annual Conference on Neural Information Processing Systems. Cambridge, Massachusetts:MIT Press, 2016:3738-3746.
[15] WANG H, SHI X J, YEUNG D Y. Collaborative recurrent autoencoder:Recommend while learning to fill in the blanks[C]//Proceedings of the 29th Annual Conference on Neural Information Processing Systems. Cambridge, Massachusetts:MIT Press, 2016:415-423.

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