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Credibility Evaluation Algorithm Based on Deep Learning

Received: 13 August 2017     Published: 17 August 2017
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Abstract

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.

Published in Applied and Computational Mathematics (Volume 6, Issue 4)
DOI 10.11648/j.acm.20170604.19
Page(s) 208-214
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Reliability, Average User, Deep Learning, Accuracy

References
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[3] Miao Xinjie, The Research and Application of Collaborative Filtering Algorithm. Nanjing: Nanjing University of Information Science & Technology, 2014.
[4] 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.
[5] L. K. Saul, T. Jaakkola, and M. I. Jordan, Mean field theory for sigmoid belief networks. Arxiv preprint cs/9603102, 1996.
[6] Zhou Tao, Ren Jie, Medo M, et al., “Bipartite network projection and personal recommendation,” Physical Review E, 2007, 76 (4 Pt 2): 046115.
[7] 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.
[8] Hinton G, Salakhutdinov R, “Reducing the dimensionality of data with neural network,” Science, 2006, 313 (504), Doi: 10, 1126/science, 1127647.
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Cite This Article
  • APA Style

    Liu Mengling, Li Zhendong. (2017). Credibility Evaluation Algorithm Based on Deep Learning. Applied and Computational Mathematics, 6(4), 208-214. https://doi.org/10.11648/j.acm.20170604.19

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    ACS Style

    Liu Mengling; Li Zhendong. Credibility Evaluation Algorithm Based on Deep Learning. Appl. Comput. Math. 2017, 6(4), 208-214. doi: 10.11648/j.acm.20170604.19

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    AMA Style

    Liu Mengling, Li Zhendong. Credibility Evaluation Algorithm Based on Deep Learning. Appl Comput Math. 2017;6(4):208-214. doi: 10.11648/j.acm.20170604.19

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  • @article{10.11648/j.acm.20170604.19,
      author = {Liu Mengling and Li Zhendong},
      title = {Credibility Evaluation Algorithm Based on Deep Learning},
      journal = {Applied and Computational Mathematics},
      volume = {6},
      number = {4},
      pages = {208-214},
      doi = {10.11648/j.acm.20170604.19},
      url = {https://doi.org/10.11648/j.acm.20170604.19},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20170604.19},
      abstract = {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.},
     year = {2017}
    }
    

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    AU  - Liu Mengling
    AU  - Li Zhendong
    Y1  - 2017/08/17
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    AB  - 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.
    VL  - 6
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Author Information
  • Department of Mathematical Sciences, Tsinghua University, Beijing, China

  • School of Information and Control, Nanjing University of Information Science & Technology, Nanjing, China

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