research-article

Hybrid Recommender System based on Autoencoders

Authors:

Romaric Gaudel,

Jérémie MaryAuthors Info & Claims

DLRS 2016: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems

Pages 11 - 16

https://doi.org/10.1145/2988450.2988456

Published: 15 September 2016 Publication History

Abstract

A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings. In the last decades, few attempts where done to handle that objective with Neural Networks, but recently an architecture based on Autoencoders proved to be a promising approach. In current paper, we enhanced that architecture (i) by using a loss function adapted to input data with missing values, and (ii) by incorporating side information. The experiments demonstrate that while side information only slightly improve the test error averaged on all users/items, it has more impact on cold users/items.

References

[1]

R. P. Adams and G. E. D. I. Murray. Incorporating side information in probabilistic matrix factorization with gaussian processes. In Proc. of UAI'10, 2010.

[2]

C. M. Bishop. Neural networks for pattern recognition. Oxford univ. press, 1995.

Digital Library

[3]

C. Chen, D. Li, Y. Zhao, Q. Lv, and L. Shang. Wemarec: Accurate and scalable recommendation through weighted and ensemble matrix approximation. In Proc. of the Int. ACM SIGIR Conf. on Res. and Dev. in Information Retrieval, pages 303--312, 2015.

Digital Library

[4]

T. Chen, W. Zhang, Q. Lu, K. Chen, Z. Zheng, and Y. Yong. Svdfeature: a toolkit for feature-based collaborative filtering. JMLR, 13(1):3619--3622, 2012.

Digital Library

[5]

G. Dziugaite and D. Roy. Neural network matrix factorization. arXiv preprint arXiv:1511.06443, 2015.

[6]

X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In Proc. of AISTATS'10, pages 249--256, 2010.

[7]

X. Glorot, A. Bordes, and Y. Bengio. Deep sparse rectifier neural networks. In Proc. of AISTATS'11, pages 315--323, 2011.

[8]

C. Gomez-Uribe and N. Hunt. The netflix recommender system: Algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst., 6(4):13:1--13:19, 2015.

Digital Library

[9]

Y.-D. Kim and S. Choi. Scalable variational bayesian matrix factorization with side information. In Proc. of AISTATS'14, Reykjavik, Iceland, 2014.

[10]

Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, 2009.

Digital Library

[11]

M. A. Kramer. Nonlinear principal component analysis using autoassociative neural networks. AIChE journal, 37(2):233--243, 1991.

[12]

R. Kumar, B. K. Verma, and S. S. Rastogi. Social popularity based svd++ recommender system. International Journal of Computer Applications, 87(14), 2014.

[13]

Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521(7553):436--444, 2015.

[14]

Y. LeCun, L. Bottou, G. Orr, and K. Muller. Efficient backprop. In Neural networks: Tricks of the trade, pages 9--48. Springer, 1998.

[15]

H. Lee, A. Battle, R. Raina, and A. Ng. Efficient sparse coding algorithms. In Advances in neural information processing systems, pages 801--808, 2006.

Digital Library

[16]

J. Lee, S. Kim, G. Lebanon, and Y. Singerm. Local low-rank matrix approximation. In Proc. of ICML'13, pages 82--90, 2013.

[17]

J. Lee, M. Sun, and G. Lebanon. A comparative study of collaborative filtering algorithms. arXiv preprint arXiv:1205.3193, 2012.

[18]

D. Li, C. Chen, Q. Lv, J. Yan, L. Shang, and S. Chu. Low-rank matrix approximation with stability. In Proc. of ICML'16, 2016.

[19]

S. Li, J. Kawale, and Y. Fu. Deep collaborative filtering via marginalized denoising auto-encoder. In Proc. of CIKM'15, pages 811--820. ACM, 2015.

Digital Library

[20]

P. Lops, M. D. Gemmis, and G. Semeraro. Content-based recommender systems: State of the art and trends. In Recommender systems handbook, pages 73--105. Springer, 2011.

[21]

H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender systems with social regularization. In Proc. of the int. conf. on Web search and data mining (WSDM '11), pages 287--296, 2011.

Digital Library

[22]

V. Miranda, J. Krstulovic, H. Keko, C. Moreira, and J. Pereira. Reconstructing Missing Data in State Estimation With Autoencoders. IEEE Transactions on Power Systems, 27(2):604--611, 2012.

[23]

A. Mnih and R. Salakhutdinov. Probabilistic matrix factorization. In NIPS'07, pages 1257--1264, 2007.

Digital Library

[24]

I. Porteous and M. W. A. U. Asuncion. Bayesian matrix factorization with side information and dirichlet process mixtures. In Proc. of AAAI'10, 2010.

Digital Library

[25]

S. Rendle. Factorization machines. In Proc. of ICDM'10, pages 995--1000, 2010.

Digital Library

[26]

R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In Proc. of ICML'08, pages 880--887. ACM, 2008.

Digital Library

[27]

R. Salakhutdinov, A. Mnih, and G. Hinton. Restricted boltzmann machines for collaborative filtering. In Proc. of ICML'07, pages 791--798. ACM, 2007.

Digital Library

[28]

S. Sedhain, A. K. Menon, S. Sanner, and L. Xie. Autorec: Autoencoders meet collaborative filtering. In Proc. of Int. Conf. on World Wide Web Companion, pages 111--112, 2015.

Digital Library

[29]

N. Srivastava, G. Hinton, A. Krizhevsk, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929--1958, 2014.

Digital Library

[30]

F. Strub and J. Mary. Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs. In NIPS Workshop on Machine Learning for eCommerce, Montreal, Canada, 2015.

[31]

O. Teytaud, S. Gelly, and J. Mary. Active learning in regression, with application to stochastic dynamic programming. In A. International Conference On Informatics in Control and Robotics, editors, ICINCO and CAP, pages 373--386, 2007.

[32]

V. Tresp, S. Ahmad, and R. Neuneier. Training Neural Networks with Deficient Data. Advances in Neural Information Processing Systems 6, pages 128--135, 1994.

Digital Library

[33]

T. T. Truyen, D. Phung, and S. Venkatesh. Ordinal boltzmann machines for collaborative filtering. In Proc. of UAI'09, pages 548--556. AUAI Press, 2009.

Digital Library

[34]

A. Van den Oord, S. Dieleman, and B. Schrauwen. Deep content-based music recommendation. In Proc. of NIPS'13, pages 2643--2651, 2013.

Digital Library

[35]

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Jour. of Mach. Learn. Res., 11(3):3371--3408, 2010.

Digital Library

[36]

H. Wang, N. Wang, and D.-Y. Yeung. Collaborative deep learning for recommender systems. Proc. of Int. Conf. on Knowledge Discovery and Data Mining (KDD'14), 2014.

Digital Library

[37]

X. Wang and Y. Wang. Improving content-based and hybrid music recommendation using deep learning. In Proc. of the ACM Int. Conf. on Multimedia, pages 627--636. ACM, 2014.

Digital Library

[38]

Y. Wu, C. DuBois, A. Zheng, and M. Ester. Collaborative denoising auto-encoders for top-n recommender systems. In Proc. of WSDM'16, pages 153--162. ACM, 2016.

Digital Library

[39]

Y. Zhou, D. Wilkinson, R. Schreiber, and R. Pan. Large-scale parallel collaborative filtering for the netflix prize. In Algorithmic Aspects in Information and Management, pages 337--348. Springer, 2008.

Digital Library

[40]

F. Zhuang, D. Luo, X. Jin, H. Xiong, P. Luo, and Q. He. Representation learning via semi-supervised autoencoder for multi-task learning. In Proc. of ICDM'15, 2015.

Digital Library

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Hybrid Recommender System based on Autoencoders
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cover image ACM Other conferences

DLRS 2016: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems

September 2016

47 pages

ISBN:9781450347952

DOI:10.1145/2988450

Conference Chairs:
Alexandros Karatzoglou
Telefonica Research
,
Balázs Hidasi
Gravity Research & Development
,
Domonkos Tikk
Gravity Research Development
,
Oren Sar-Shalom
IBM Research
,
Haggai Roitman
IBM Research
,
Bracha Shapira
Ben-Gurion University
,
Lior Rokach
Ben-Gurion University

Copyright © 2016 ACM.

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Published: 15 September 2016

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DLRS 2016: Workshop on Deep Learning for Recommender Systems

September 15, 2016

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Cited By

Alrashidi MSelamat AIbrahim RFujita H(2024)Social Recommender System Based on CNN Incorporating Tagging and Contextual FeaturesJournal of Cases on Information Technology10.4018/JCIT.33552426:1(1-20)Online publication date: 7-Jan-2024
https://doi.org/10.4018/JCIT.335524
Nguyen TQuoc Viet Hung NNguyen THuynh TNguyen TWeidlich MYin H(2024)Manipulating Recommender Systems: A Survey of Poisoning Attacks and CountermeasuresACM Computing Surveys10.1145/3677328Online publication date: 25-Jul-2024
https://doi.org/10.1145/3677328
Zheng XNi ZZhong XLuo Y(2024)Kernelized Deep Learning for Matrix Factorization Recommendation System Using Explicit and Implicit InformationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318294235:1(1205-1216)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3182942
Kwon DSohn S(2024)Convergence Technology Opportunity Discovery for Firms Based on Technology Portfolio Using the Stacked Denoising AutoEncoder (SDAE)IEEE Transactions on Engineering Management10.1109/TEM.2022.320887171(1804-1818)Online publication date: 2024
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Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1109/MCI.2024.3363984
Jung NHoang VLee OChoi C(2024)Kiosk Recommend System Based on Self-Supervised Representation Learning of User Behaviors in Offline RetailIEEE Internet of Things Journal10.1109/JIOT.2024.336514411:10(18686-18697)Online publication date: 15-May-2024
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LeBlanc PBanks DFu LLi MTang ZWu Q(2024)Recommender Systems: A ReviewJournal of the American Statistical Association10.1080/01621459.2023.2279695119:545(773-785)Online publication date: 4-Jan-2024
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Jang DLi QLee CKim J(2024)Attention-based multi attribute matrix factorization for enhanced recommendation performanceInformation Systems10.1016/j.is.2023.102334121(102334)Online publication date: Mar-2024
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Shirali AKazemi RAmini A(2024)Collaborative Filtering With Representation Learning in the Frequency DomainInformation Sciences10.1016/j.ins.2024.121240(121240)Online publication date: Jul-2024
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Bhatia V(2024)DLSFExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123900250:COnline publication date: 18-Jul-2024
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