Applied and Computational Mathematics
Volume 6, Issue 4, August 2017, Pages: 208-214
Received: Aug. 13, 2017;
Published: Aug. 17, 2017
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Liu Mengling, Department of Mathematical Sciences, Tsinghua University, Beijing, China
Li Zhendong, School of Information and Control, Nanjing University of Information Science & Technology, Nanjing, China
The credibility of a recommendation system is a hot focus nowadays in the field of personalized recommendation research. However, it is difficult to carry out effective credibility evaluation for the users in the presence of a false recommendation system, say nothing of eliminating suspicious users and further more improve the security and reliability of the system. This paper proposed a new method of reliability assessment based on deep learning. According to the users’ rating database, community of users with average scores is constructed and traditional credibility algorithm is used to calculate the initial credibility of the users. With the average users' reliability value as a criterion, the second assessment to the credibility based on deep learning algorithm is applied to other users, the results of which are arranged in ascending order. Then suspicious users ranking top-L will be removed and a trustfully adjacent group for the target users will be created. Experiments show that the improved algorithm can optimize the recommendation system with better security, accuracy and reliability as well.
Credibility Evaluation Algorithm Based on Deep Learning, Applied and Computational Mathematics.
Vol. 6, No. 4,
2017, pp. 208-214.
Qin Jiwei, Zheng Qinghua, et al., “A collaborative recommendation algorithm based on ratings and trust,” Joumal of Xi’an Jiaotong University, 2013, 47 (4), pp. 100-104.
Liu Shengzong, Liao Zhifang, Wu Yanfeng, “A Collaborative Filtering Algorithm Combined with User Rating Credibility and Similarity,” Journal of Chinese Computer Systems, 2014, 35 (5), pp. 973-977.
Miao Xinjie, The Research and Application of Collaborative Filtering Algorithm. Nanjing: Nanjing University of Information Science & Technology, 2014.
R. Salakhutdinov, A. Mnih, and G. Hinton. Restricted boltzmann machines for collaborative filtering. In Proceedings of the 24th international conference on Machine learning, pp. 791–798. ACM, 2007.
L. K. Saul, T. Jaakkola, and M. I. Jordan, Mean field theory for sigmoid belief networks. Arxiv preprint cs/9603102, 1996.
Zhou Tao, Ren Jie, Medo M, et al., “Bipartite network projection and personal recommendation,” Physical Review E, 2007, 76 (4 Pt 2): 046115.
Victor P, Verbiest N, Cornelis C, et al., “Enhancing the trust-based recommendation process with explicit distrust,” ACM Transactions on the Web (TWEB), 2013, 7 (2), pp. 61-80.
Hinton G, Salakhutdinov R, “Reducing the dimensionality of data with neural network,” Science, 2006, 313 (504), Doi: 10, 1126/science, 1127647.
Geoffrey E. Hinton, Simon Osindero, Yee-Whye The, “A Fast Learning Algorithm For Deep Belief Nets,” Neural Computation 18, 2006, pp. 1527-1554.
Ruslan Salakhutdinov, Andriy Mnih, Geoffrey Hinton, “Restricted Boltzmann Machines for Collaborative Filtering,” Proceedings of the 24th International Conference on Machine Learning, Corvallis, OR, 2007.
Yu Kai, Jia Lei, Chen Yuqiang, “Deep Learning: Promote the dream of artificial intelligence,” Programmer, 2013 (6), pp. 22-27.
Wang Shengzhu, Li Yong-zhong, “Intrusion detection algorithm based on deep learning and semi-supervised learning,” Information Technology, 2017 (1), pp. 101-104, 108.
Chen Hong, Wan Guangxue, “Intrusion detection method of deep belief network model based on optimization of data processing,” Journal of Computer Applications, 2017, 37 (6), pp. 1636-1643, 1656.