Abstract
The sparseness of the rating data seriously affects the recommendation accuracy of the collaborative filtering algorithm. Since the current recommendation algorithm does not fully consider the item attribute matrix information, this paper proposes a Singular Value Decomposition (SVD) algorithm combined with Stacked Denoising Autoencoder (SDAE). The S-SVD algorithm first reduces the original user-item rating matrix by SVD. Then, we use SDAE for feature learning to calculate the similarity between the rating-based and attribute-based items on the item matrix. So, we can calculate their item the similarity between any two items which can be films, books and so on. Finally, we get the nearest neighbor set of unrated items and predict the rating to generate recommendations. Experiments on the real datasets show the S-SVD algorithm can overcome the data sparsity problem to a large extent and is superior to other traditional recommendation algorithms in performance.
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References
Liu, Y., Wang, S., Khan, M.S., et al.: A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Min. Anal. 1(3), 211–221 (2018)
He, X., Liao, L., Zhang, H., et al.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp. 173–182 (2017)
Gong, A., Gao, Y., Gao, H.F.: A collaborative filtering recommendation algorithm based on project attribute scoring. Comput. Eng. Sci. 37(12), 2366–2371 (2015)
Wei, G.M., Liu, Z., Li, L.F., Zhang, M.: A singular value decomposition recommendation algorithm based on user preferences for project attributes. J. Xi’an Jiaotong Univ. 52(05), 101–107 (2018)
Zheng, Y., Tang, B., Ding, W., et al.: A neural autoregressive approach to collaborative filtering. arXiv preprint arXiv:1605.09477. (2016)
Sun, X.H., Chen, H., Kong, F.S.: Combining singular value decomposition and nearest neighbor method in collaborative filtering, Application Research of Computers, pp. 206–208 (2006)
Vincent, P., Larochelle, H., Lajoie, I., et al.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(Dec), 3371–3408 (2010)
Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235–1244. ACM (2015)
Sarwar, B., Karypis, G., Konstan, J., et al.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web. ACM, pp. 285–295 (2001)
Bobadilla, J., Ortega, F., Hernando, A., et al.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52, Morgan Kaufmann Publishers Inc. (1998)
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Hu, S., Song, Z. (2020). Singular Value Decomposition Algorithm Combined with SDAE for Improving the Accuracy of Movie Recommended System. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019. ATCI 2019. Advances in Intelligent Systems and Computing, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-25128-4_166
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DOI: https://doi.org/10.1007/978-3-030-25128-4_166
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