Wide and Deep Learning for Recommender Systems

From statwiki
Revision as of 11:40, 29 November 2021 by J47pan (talk | contribs)
Jump to navigation Jump to search
The printable version is no longer supported and may have rendering errors. Please update your browser bookmarks and please use the default browser print function instead.

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.