Table of Contents Author Guidelines Submit a Manuscript
Scientific Programming
Volume 2017, Article ID 4379141, 9 pages
https://doi.org/10.1155/2017/4379141
Research Article

A Novel Hybrid Similarity Calculation Model

1School of Information Science and Engineering, Central South University, Hunan, China
2Hunan University of Finance and Economics, Hunan, China
3School of Software, Central South University, Hunan, China

Correspondence should be addressed to Zhifang Liao; nc.ude.usc@oailfz

Received 25 August 2017; Accepted 12 November 2017; Published 4 December 2017

Academic Editor: Longxiang Gao

Copyright © 2017 Xiaoping Fan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. Luo, C. Niu, R. Shen, and C. Ullrich, “A collaborative filtering framework based on both local user similarity and global user similarity,” Machine Learning, vol. 72, no. 3, pp. 231–245, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. D. Anand and K. K. Bharadwaj, “Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities,” Expert Systems with Applications, vol. 38, no. 5, pp. 5101–5109, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. A. R. S. Lopes, R. B. C. Prudencio, and B. L. D. Bezerra, “A collaborative filtering framework based on local and global similarities with similarity tie-breaking criteria,” in Proceedings of the International Joint Conference on Neural Networks, pp. 2887–2893, July 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. H. Li, G. Wang, and M. Gao, “A novel similarity calculation for collaborative filtering,” in Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, pp. 38–43, IEEE, July 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Shen, Y. Wei, and Y. Yang, “Collaborative filtering recommendation algorithm based on two stages of similarity learning and its optimization,” in Proceedings of the 13th IFAC Symposium on Large Scale Complex Systems: Theory and Applications, pp. 335–340, July 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. L. Gao and M. Huang, “A collaborative filtering recommendation algorithm with time adjusting based on attribute center of gravity model,” in Proceedings of the 12th Web Information System and Application Conference, pp. 197–200, September 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. X. Luo, J. Deng, J. Liu, W. Wang, X. Ban, and J. Wang, “A quantized kernel least mean square scheme with entropy-guided learning for intelligent data analysis,” China Communications, vol. 14, no. 7, pp. 1–10, 2017. View at Publisher · View at Google Scholar
  8. J. Beel, S. Langer, A. Nürnberger et al., “The impact of demographics (age and gender) and other user-characteristics on evaluating recommender systems,” in Research and Advanced Technology for Digital Libraries, pp. 396–400, Springer, Berlin, Germany, 2013. View at Google Scholar
  9. Y. Wang, S. C.-F. Chan, and G. Ngai, “Applicability of demographic recommender system to tourist attractions: a case study on trip advisor,” in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, pp. 97–101, December 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. W. Zhao, R. Lun, C. Gordon et al., “A human-centered activity tracking system: toward a healthier workplace,” IEEE Transactions on Human-Machine Systems, vol. 47, no. 3, pp. 343–355, 2017. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Y. Al-Shamri, “User profiling approaches for demographic recommender systems,” Knowledge-Based Systems, vol. 100, pp. 175–187, 2016. View at Publisher · View at Google Scholar
  12. E. B. Santos, M. Garcia Manzato, and R. Goularte, “Evaluating the impact of demographic data on a hybrid recommender model,” IADIS International Journal on WWW/Internet, vol. 12, no. 2, pp. 149–167, 2014. View at Google Scholar
  13. T. Chen and L. He, “Collaborative filtering based on demographic attribute vector,” in Proceedings of the International Conference on Future Computer and Communication, pp. 225–229, IEEE Computer Society, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. X. Luo, H. Luo, and X. Chang, “Online optimization of collaborative web service QoS prediction based on approximate dynamic programming,” in Proceedings of the International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI '14), pp. 80–83, IEEE, Beijing, China, October 2014. View at Publisher · View at Google Scholar
  15. X. Luo, J. Liu, D. D. Zhang, and X. Chang, “A large-scale web QoS prediction scheme for the Industrial Internet of Things based on a kernel machine learning algorithm,” Computer Networks, vol. 101, pp. 81–89, 2016. View at Publisher · View at Google Scholar
  16. L. Y. Dou and X. H. Wang, “A collaborative filtering recommendation algorithm based on the context of time and tags,” Journal of Taiyuan University of Technology, no. 6, 2015. View at Google Scholar
  17. Y. Koren, “Collaborative filtering with temporal dynamics,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '09), pp. 447–456, Paris, France, June 2009. View at Publisher · View at Google Scholar
  18. A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver, “Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering,” in Proceedings of the 4th ACM Recommender Systems Conference (RecSys '10), pp. 79–86, ACM, Barcelona,Spain, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. L. Xiong, X. Chen, T. K. Huang et al., “Temporal collaborative filtering with bayesian probabilistic tensor factorization,” in Proceedings of the Siam International Conference on Data Mining (SDM '10), pp. 211–222, Columbus, Ohio, USA, April-May 2010. View at Publisher · View at Google Scholar
  20. H. G. Rong, S. X. Huo, C. H. Hu et al., “User similarity-based collaborative filtering recommendation algorithm,” Journal on Communications, vol. 35, no. 2, pp. 16–24, 2014. View at Google Scholar
  21. B. Li, X. Zhu, R. Li et al., “Cross-domain collaborative filtering over time,” in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI '11), pp. 2293–2298, Barcelona, Spain, July 2011.
  22. M. Jamali and M. Ester, “TrustWalker: a random walk model for combining trust-based and item-based recommendation,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '09), pp. 397–405, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. H. Yu and Z. Y. Li, “A collaborative filtering recommendation algorithm based on forgetting curve,” Journal of Nanjing University (Natural Sciences), vol. 46, no. 5, pp. 520–527, 2010. View at Google Scholar
  24. W. Wei, X. Fan, H. Song, X. Fan, and J. Yang, “Imperfect information dynamic stackelberg game based resource allocation using hidden markov for cloud computing,” IEEE Transactions on Services Computing, 2016. View at Publisher · View at Google Scholar
  25. T. Li, Y. Liu, L. Gao, and A. Liu, “A cooperative-based model for smart-sensing tasks in fog computing,” IEEE Access, vol. 5, pp. 21296–21311, 2017. View at Publisher · View at Google Scholar
  26. D.-J. Yao, J. Yang, and X.-J. Zhan, “Feature selection algorithm based on random forest,” Journal of Jilin University (Engineering and Technology Edition), vol. 44, no. 1, pp. 137–141, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. H. J. Ahn, “A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem,” Information Sciences, vol. 178, no. 1, pp. 37–51, 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. H. Liu, Z. Hu, A. Mian, H. Tian, and X. Zhu, “A new user similarity model to improve the accuracy of collaborative filtering,” Knowledge-Based Systems, vol. 56, pp. 156–166, 2014. View at Publisher · View at Google Scholar · View at Scopus