Abstract
Recommender systems are widely used in our life for automatically recommending items relevant to our preference. Matrix Factorization (MF) is one of the most successful methods in recommendation. However, the rating matrix utilized by the MF-based models is usually sparse, so it is of vital significance to integrate the side information to provide relatively effective knowledge for modeling the user or item features. The key problem lies how to extract representative features from the noisy side information. In this paper, we propose Stacked Discriminative Denoising Auto-encoder based Recommender System (SDDRS) by integrating deep learning model with MF based recommender system to effectively incorporate side information with rating information. Extensive top-N recommendation experiments conducted on three real-world datasets empirically demonstrate that SDDRS outperforms several state-of-the-art methods.
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Notes
- 1.
According to [18], for the reason of private, usually the useful user side information is hard to access, and thus we only consider the item side information in this paper.
- 2.
References
Li, M., Huang, D., Wei, B., Wang, C.D.: Event recommendation via collective matrix factorization with event-user neighborhood. In: IScIDE, pp. 676–686 (2017)
Balabanovic, M., Shoham, Y.: Content-based collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Hu, Q.Y., Zhao, Z.L., Wang, C.D., Lai, J.H.: An item orientated recommendation algorithm from the multi-view perspective. Neurocomputing 269, 261–272 (2017)
Zhao, Z.L., Wang, C.D., Lai, J.H.: AUI&GIV: recommendation with asymmetric user influence and global importance value. PLoS One 11(2), e0147944 (2016)
Zhao, Z.L., Wang, C.D., Wan, Y.Y., Lai, J.H., Huang, D.: FTMF: recommendation in social network with feature transfer and probabilistic matrix factorization. In: IJCNN, 847–854 (2016)
Zhao, Z.L., Wang, C.D., Wan, Y.Y., Lai, J.H.: Recommendation in feature space sphere. Electron. Commer. Res. Appl. 26, 109–118 (2017)
Zhao, Z.-L., Huang, L., Wang, C.-D., Huang, D.: Low-rank and sparse cross-domain recommendation algorithm. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds.) DASFAA 2018. LNCS, vol. 10827, pp. 150–157. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91452-7_10
Wang, C.D., Deng, Z.H., Lai, J.H., Yu, P.S.: Serendipitous recommendation in e-commerce using innovator-based collaborative filtering. IEEE Trans. Cybern. (2018, in press)
Zhao, Z.-L., Huang, L., Wang, C.-D., Lai, J.-H., Yu, P.S.: Low-rank and sparse matrix completion for recommendation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) ICONIP 2017. LNCS, vol. 10638, pp. 3–13. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70139-4_1
Xu, Y.-N., Xu, L., Huang, L., Wang, C.-D.: Social and content based collaborative filtering for point-of-interest recommendations. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) ICONIP 2017. LNCS, vol. 10638, pp. 46–56. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70139-4_5
Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)
Bengio, Y., Yao, L., Alain, G., Vincent, P.: Generalized denoising auto-encoders as generative models. In: NIPS, pp. 899–907 (2013)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)
Xue, H.J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. IJCA I, 3203–3209 (2017)
Burke, R.D.: Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002)
Li, M.R., Huang, L., Wang, C.D.: Geographical and overlapping community modeling based on business circles for POI recommendation. In: IScIDE, pp. 665–675 (2017)
de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Rueda-Morales, M.A.: Combining content-based and collaborative recommendations: a hybrid approach based on bayesian networks. Int. J. Approx. Reason. 51(7), 785–799 (2010)
Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: KDD, pp. 1235–1244 (2015)
Salakhutdinov, R., Mnih, A., Hinton, G.E.: Restricted Boltzmann machines for collaborative filtering. In: ICML, pp. 791–798 (2007)
Wei, J., He, J., Chen, K., Zhou, Y., Tang, Z.: Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst. Appl. 69, 29–39 (2017)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: ICML, pp. 1096–1103 (2008)
Erhan, D., Bengio, Y., Courville, A.C., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11, 625–660 (2010)
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)
Dong, X., Yu, L., Wu, Z., Sun, Y., Yuan, L., Zhang, F.: A hybrid collaborative filtering model with deep structure for recommender systems. AAA I, 1309–1315 (2017)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)
Ning, X., Karypis, G.: SLIM: sparse linear methods for top-n recommender systems. In: ICDM, pp. 497–506 (2011)
Ning, X., Karypis, G.: Sparse linear methods with side information for top-n recommendations. In: RecSys, pp. 155–162 (2012)
Wu, Y., DuBois, C., Zheng, A.X., Ester, M.: Collaborative denoising auto-encoders for top-n recommender systems. In: WSDM, pp. 153–162 (2016)
Acknowledgements
This work was supported by NSFC (61502543), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014), Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (2016TQ03X542), and the key research project of Guangzhou municipal colleges and universities “The Research and realization of 3D Game Engine based on Android” (No. 2012A164).
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Wang, K., Xu, L., Huang, L., Wang, CD., Lai, JH. (2018). Stacked Discriminative Denoising Auto-encoder Based Recommender System. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_24
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