Open Access
MATEC Web of Conferences
Volume 22, 2015
International Conference on Engineering Technology and Application (ICETA 2015)
Article Number 01013
Number of page(s) 7
Section Information and Communication Technology
Published online 09 July 2015
  1. Lv H.L., Wang J.L. & Deng F, 2012. A Recommendation Algorithm for Individualized Travelling Route. Network New Media Technology. 1(3): 42–48. [Google Scholar]
  2. Liu J.G., Zhou T. & Wang B.H. 2009. Development Progress of Individualized Recommendation System. Natural Science Progress. 19(1): 1–15. [CrossRef] [Google Scholar]
  3. Adomavicius G. & Tuzhilin. A. 2005. Toward the next generation of recommender systems: A survey of the state-of-the art and possible extensions. IEEE transactions on knowledge and data engineering. pp: 734–749. [Google Scholar]
  4. Linden G, Smith B. & York J. 2003. recommendations: Item-to-item collaborative filtering. IEEE Internet computing. 7(1): 76–80. [Google Scholar]
  5. Said A, Berkovsky S. & De Luca E W. 2010. Putting things in context: Challenge on context-aware movie recommendation. ACM. [Google Scholar]
  6. Su J H, Yeh H H. & Yu P S, et al. 2010. Music recommendation using content and context information mining. Intelligent Systems. IEEE. 25(1): 16–26. [Google Scholar]
  7. Shi Y.F., Wen Y.M., Cai G.Y. & Miu Y.Q. 2014. Collaborative Filtering Recommendation Based on Scenic Spot Label. Computer Application. 34(10): 2854–2858. [Google Scholar]
  8. Xu P.Y. & Dang Y.Z. 2011. Recommendation Algorithm Based on Element Similarity. Computer Application Research. 28(10): 3646–3659. [Google Scholar]
  9. Massa P. & Avesani P. 2007. Trust-aware recommender systems. Proceedings of the 2007 ACM Conference on Recommender Systems. New York: ACM Press. [Google Scholar]
  10. Sarwar B, Karypis G. & Konstan J, et al. 2001. Item-Based Collaborative Filtering Recommendation Algorithms. Proceedings of the 10th International World Wide Web Conference. New York. [Google Scholar]
  11. Breese J, Hecherman D. & Kadie C. 1998. Empirical Analysis of Forecast Algorithms for Collaborative Filtering. Proceedings of the 14th Conference on Uncertainty in Artifical Intelligence (UAI 98). [Google Scholar]
  12. Zeng L.W., Wang D. & Wu J. 2013. Analysis of Travelling Route Recommendation Model Practice Based on Apiori Algorithm. Computer Knowledge and Technology. pp: 1906–1908. [Google Scholar]
  13. Shardanand U. & Maes P. 1995. Social Information Filtering: Algorithms for Automating ‘World of Mouth’. Proceeding of the Conference on Human Factors in Computing Systems. [Google Scholar]
  14. Ctrip. [EB/OL]. [2014-02-12]. [Google Scholar]
  15. Victor P, Cock M D. & Cornells C. 2011. Trust and recommendations. // Kantor P B, Rokach L, Ricci F, et al. Recommender Systems Handbook. Berlin: Springer. [Google Scholar]
  16. Jarvelin K. & Kekalainen J. 2002. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS). 20(4): 422–446. [Google Scholar]
  17. Jamali M. & Ester M. 2009. Trustwalker: a random walk model for combining trust-based and item-based recommendation.// Proceedings of 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press. [Google Scholar]

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