Open Access
Issue
ITM Web Conf.
Volume 7, 2016
3rd Annual International Conference on Information Technology and Applications (ITA 2016)
Article Number 05008
Number of page(s) 5
Section Session 5: Algorithms and Simulation
DOI https://doi.org/10.1051/itmconf/20160705008
Published online 21 November 2016
  1. Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. 1992. Using collaborative filtering to weave an information tapestry. Comm. ACM 35, 12, 61–70. [Google Scholar]
  2. Linden, G., Smith, B., and York, J. 2003. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7, 1, 76–80. [Google Scholar]
  3. Ali, K. and Van Stam, W. Tivo: making show recommendations using a distributed collaborative filtering architecture. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 2004, 394–401. [Google Scholar]
  4. Herlocker, Jonathan L., Joseph A. Konstan, Al Borchers, and John Riedl. “An algorithmic framework for performing collaborative filtering.” In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 230–237. ACM, 1999. [Google Scholar]
  5. Sarwar, Badrul, George Karypis, Joseph Konstan, and John Riedl. “Item-based collaborative filtering recommendation algorithms.” In Proceedings of the 10th international conference on World Wide Web, ACM, 2001, 285–295 [Google Scholar]
  6. Koren, Yehuda. “Factorization meets the neighborhood: a multifaceted collaborative filtering model.” In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2008, 426–434. [CrossRef] [Google Scholar]
  7. Chen, Chunan, Weiwei Sun, Baihua Zheng, Dingding Mao, and Weimo Liu. “An incremental approach to closest pair queries in spatial networks using best-first search.” In International Conference on Database and Expert Systems Applications, pp. 136–143. Springer Berlin Heidelberg, 2011. [Google Scholar]
  8. Chang, Te-Min, and Wen-Feng Hsiao. “LDA-based Personalized Document Recommendation.”, 2013. [Google Scholar]
  9. Liu, Qi, et al. “Enhancing collaborative filtering by user interest expansion via personalized ranking.” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 42.1, 2012, 218–233. [CrossRef] [Google Scholar]
  10. Ortega, F., Bobadilla, J., Hernando, A., and Guti’errez, A. Incorporating group recommendations to recommender systems: Alternatives and performance. Information Processing & Management, 2013, 49(4): 895–901. [CrossRef] [Google Scholar]
  11. Wang Z, Liao J, Cao Q, et al. Friendbook: A Semantic-Based Friend Recommendation System for Social Networks[J]. IEEE Transactions on Mobile Computing, 2015, 14(3):538–551. [CrossRef] [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.