Open Access
SHS Web of Conferences
Volume 14, 2015
ICITCE 2014 – International Conference on Information Technology and Career Education
Article Number 01002
Number of page(s) 5
Section Career Education
Published online 07 January 2015
  1. C.D. Manning, P. Raghavan & H. Schutze. Introduction to Information Retrieva [M]. England: Cambridge University Press, 2008. [Google Scholar]
  2. J.B. Schafe, L.J. Konstan & J. Ried. E-commerce recommendation applications[J]. Data Mining and Knowledge Discovery, 2001, 5(1–2):115–153. [CrossRef] [Google Scholar]
  3. B.M. Sarwar & Karypis. Item-based collaborative filtering recommendation algorithms [C]. Proceedings of the 10th International Conference on World Wide Web, N.Y, USA: ACM, 2001: 285+295. [Google Scholar]
  4. G Linden, B. Smith & J. York. Recommendations: Item to item collaborative filtering [J]. IEEE Internet Computing, 2003, 7(1):76–80. [CrossRef] [MathSciNet] [Google Scholar]
  5. Zhao Chenting & Ma Chune. Exploring the Secret inside the Recommendation Engine: In-depth Study on the Algorithm Relevant to Recommendation Engine–collaborative filtering [Online] Available from: [Google Scholar]
  6. M. Balabanovic & Y. Shoham. Fab: Content–based, collaborative recommendation [J]. Communications of the ACM, 1997, 40(3):66–72. [Google Scholar]
  7. R. Melville & R.J. Mooney. Contented–based collaborative filtering for improved recommendations [C]. Proceedings of the 18th National Conference on Artificial Intelligence, 2002, pp.189–192. [Google Scholar]
  8. B.M. Kim, Q. Li & C.S. Park. A new approach for combining content–based and collaboration filters [J]. Journal of Intelligent Information System, 2006, 27(1):79–91. [CrossRef] [Google Scholar]
  9. H. Lyle & PI Dean. Clustering methods for collabo-rative filtering[C]. In: Workshop on Recommendation Systems at the Fifteenth National Conference on Artificial Systems, 1998, pp.114–129. [Google Scholar]
  10. M. Vozalis & K. Margaritis. Applying SVD on Item–based filtering[C]. Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, 2005, pp.464–469. [Google Scholar]
  11. R. Paulson, A. Tzanavari. Combining collaborative and content–Based filtering using conceptual graphs [J]. Modelling with Work. 2003:168–185. [CrossRef] [Google Scholar]
  12. Huang Zan & Chung Wingyan. A Graph Model for E-Commerce Recommender Systems[J]. J. ASIST, 2003, 55(3):259–274. [Google Scholar]
  13. R Resnick & H.R Varian. Recommender systems[J]. Communications of the ACM, 1997, 40(3):56–58. [CrossRef] [Google Scholar]
  14. Wu Lihua & Liu Lu. Modeling overview of personalized recommendation system users [J]. Information Journal, 2006, 25(1):55–56. [Google Scholar]
  15. G. Adomavicius & A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state of the art and possible extensions [J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 7(6):734–749. [Google Scholar]
  16. R.M. Bell & Y. Koren. Improved neighborhood–based collaborative filtering [C]. KDD Cup’07, San Jose, California, USA, August 12, 2007, pp.7+14. [Google Scholar]
  17. S. Baluja & R. Seth. Video suggestion and discovery for youtube: Taking random walks through the view graph [J]. Proceedings of 17th Intel World Wide Web Conference, 2008, pp.895–904. [Google Scholar]
  18. F. Fouss & A. Pirotte. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation [J]. IEEE Transactions, 19, 2007:355–369. [Google Scholar]
  19. Zhou Junjun, Wang Mingwen & He Shizhu. Collaborative filtering recommendation algorithm based on random walk and clustering smoothing [J]. Journal of Guangxi Normal University (Natural Science Edition), 2011, 29(1):173–178. [Google Scholar]
  20. Liu Jianguo, Zhou Tao, Guo Qiang & Wang Bing-hong. Overview of evaluation methods of the personalized recommendation system [J]. Complex Systems and Complexity Science, 2009, 6(3):1–10. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.