Difference between revisions of "Wide and Deep Learning for Recommender Systems"

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== Introduction ==
 
== Introduction ==
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This paper presents a jointly trained wide linear models and deep neural networks architecture - Wide & Deep Learning. This newly designed architecture can achieve both memorization and generalization for recommender systems
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== Related Work ==
  
 
abc
 
abc
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== Model Architecture ==
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abc
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== Model Results ==
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abc
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== Conclusion ==
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abc
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== Critiques ==
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abc
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== References ==
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[1] J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12:2121–2159, July 2011.
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[2] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2016.
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[3] H. B. McMahan. Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization. In Proc. AISTATS, 2011.
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[4] T. Mikolov, A. Deoras, D. Povey, L. Burget, and J. H. Cernocky. Strategies for training large scale neural network language models. In IEEE Automatic Speech Recognition & Understanding Workshop, 2011.
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[5] S. Rendle. Factorization machines with libFM. ACM Trans. Intell. Syst. Technol., 3(3):57:1–57:22, May 2012.
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[6] J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler. Joint training of a convolutional network and a graphical model for human pose estimation. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, editors, NIPS, pages 1799–1807. 2014.
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[7] H. Wang, N. Wang, and D.-Y. Yeung. Collaborative deep learning for recommender systems. In Proc. KDD, pages 1235–1244, 2015.
 +
 +
[8] B. Yan and G. Chen. AppJoy: Personalized mobile application discovery. In MobiSys, pages 113–126, 2011.

Revision as of 11:40, 29 November 2021

Presented by

Junbin Pan

Introduction

This paper presents a jointly trained wide linear models and deep neural networks architecture - Wide & Deep Learning. This newly designed architecture can achieve both memorization and generalization for recommender systems


Related Work

abc

Model Architecture

abc

Model Results

abc

Conclusion

abc

Critiques

abc

References

[1] J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12:2121–2159, July 2011.

[2] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2016.

[3] H. B. McMahan. Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization. In Proc. AISTATS, 2011.

[4] T. Mikolov, A. Deoras, D. Povey, L. Burget, and J. H. Cernocky. Strategies for training large scale neural network language models. In IEEE Automatic Speech Recognition & Understanding Workshop, 2011.

[5] S. Rendle. Factorization machines with libFM. ACM Trans. Intell. Syst. Technol., 3(3):57:1–57:22, May 2012.

[6] J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler. Joint training of a convolutional network and a graphical model for human pose estimation. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, editors, NIPS, pages 1799–1807. 2014.

[7] H. Wang, N. Wang, and D.-Y. Yeung. Collaborative deep learning for recommender systems. In Proc. KDD, pages 1235–1244, 2015.

[8] B. Yan and G. Chen. AppJoy: Personalized mobile application discovery. In MobiSys, pages 113–126, 2011.