Abstract
The matrix factorization recommender system based on manifold regularization, taking into account the similarity of local neighbors and manifold structure, can improve the quality of a recommendation system. However, the similarity between samples may not be accurate due to the sparsity of the data or the incompleteness of the tag information. Therefore, we propose a new model called SI-GMF (Similarity-learning-based Improved Graph Regularized matrix Factorization) by embedding the new similarity measure strategy in GMF (Graph Regularized matrix Factorization) framework, and induce three new matrix factorization algorithms (SI-GMF_1, SI-GMF_2, SI-GMF_3) based on three initial similarities by employing three different similarity measures. The solutions to the newly developed algorithms can be effectively obtained by SGD method. The experimental results show that the newly designed algorithms significantly improve the accuracy of a recommender system.
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References
Puglisi, S., Parra-Arnau, J., Forné, J., Rebollo-Monedero, D.: On content-based recommendation and user privacy in social-tagging systems. Comput. Stand. Interfaces 41, 17–27 (2015)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Ji, H., Li, J.F., Ren, C., He, M.: Hybrid collaborative filtering model for improved recommendation. In: IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 142–145 (2013)
Bokde, D., Girase, S., Mukhopadhyay, D.: Matrix factorization model in collaborative filtering algorithms: a survey ☆. Procedia Comput. Sci. 49(1), 136–146 (2015)
Wu, M.: Collaborative filtering via ensembles of matrix factorizations. In: Proceedings of Kdd Cup & Workshop, vol. 30, pp. 29–38 (2007)
Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of Kdd Cup & Workshop (2007)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
He, X.: Locality preserving projections. Adv. Neural Inf. Process. Syst. 45(1), 186–197 (2005)
Hu, W., Choi, K.S., Wang, P., Jiang, Y., Wang, S.: Convex nonnegative matrix factorization with manifold regularization. Neural Netw. Official J. Int. Neural Netw. Soc. 63C(1), 94–103 (2014)
Wei, L., Yin, J., Deng, S., Li, Y.: Collaborative web service QoS prediction with location-based regularization. In: IEEE International Conference on Web Services, pp. 464–471. IEEE (2012)
Yin, J., Wei, L., Deng, S., Li, Y., Wu, Z., Xiong, N.: Colbar: a collaborative location-based regularization framework for QoS prediction. Inf. Sci. 265(5), 68–84 (2014)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Acknowledgments
The research was supported by the National Natural Science Foundation of China under Grants 61432008, 61272222.
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Tao, Y., Yang, M. (2016). An Improved Recommender Model by Joint Learning of Both Similarity and Latent Feature Space. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_40
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DOI: https://doi.org/10.1007/978-3-319-46257-8_40
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