Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2013, Article ID 248084, 8 pages
http://dx.doi.org/10.1155/2013/248084
Research Article

Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation

1Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA
2Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK

Received 14 June 2013; Accepted 22 June 2013

Academic Editor: Dexing Kong

Copyright © 2013 Jinyu Hu 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. A. K. Milicevic, A. Nanopoulos, and M. Ivanovic, “Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions,” Artificial Intelligence Review, vol. 33, no. 3, pp. 187–209, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. A. K. Menon, K. Chitrapura, S. Garg, D. Agarwal, and N. Kota, “Response prediction using collaborative filtering with hierarchies and side-information,” in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '11), pp. 141–149, San Diego, Calif, USA, August 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. X. Ning and G. Karypis, “SLIM: sparse linear methods for top-n recommender systems,” in Proceedings of the 11th IEEE International Conference on Data Mining (ICDM '11), pp. 497–506, Vancouver, Canada, December 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Bobadilla, F. Serradilla, and A. Hernando, “Collaborative filtering adapted to recommender systems of e-learning,” Knowledge-Based Systems, vol. 22, no. 4, pp. 261–265, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. G. Chen, F. Wang, and C. Zhang, “Collaborative filtering using orthogonal nonnegative matrix tri-factorization,” Information Processing and Management, vol. 45, no. 3, pp. 368–379, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. L. Candillier, F. Meyer, and M. Boullé, “Comparing state-of-the-art collaborative filtering systems,” in Proceedings of the International Conference on Machine Learning and Data Mining, pp. 548–562, Leipzig, Germany, July 2007.
  7. K. Choi and Y. Suh, “A new similarity function for selecting neighbors for each target item in collaborative filtering,” Knowledge-Based Systems, vol. 37, no. 1, pp. 146–153, 2013. View at Google Scholar
  8. D. Helic, “Managing collaborative learning processes in e-learning applications,” in Proceedings of the 29th IEEE International Conference on Information Technology Interfaces, pp. 345–350, Dubrovnik, Croatia, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. C. Cechinela, M. Siciliab, and S. Sánchez-Alonsob, “Evaluating collaborative filtering recommendations inside large learning object repositories,” Information Processing and Management, vol. 49, no. 1, pp. 34–50, 2013. View at Google Scholar
  10. A. Rajput, M. Dubey, S. Thakur, and M. Gondgly, “Improved item based collaboration filtering using recommendation system,” Binary Journal of Data Mining and Networking, vol. 1, no. 1, pp. 6–13, 2011. View at Google Scholar
  11. R. Jäschke, A. Hotho, F. Mitzlaff, and G. Stumme, “Challenges in tag recommendations for collaborative tagging systems: recommender systems for the social web,” Intelligent Systems Reference Library, vol. 32, pp. 65–87, 2012. View at Google Scholar
  12. D. Zhang and C. Xu, “A collaborative filtering recommendation system by unifying user similarity and item similarity,” in Web-Age Information Management, vol. 7142 of Lecture Notes in Computer Science, pp. 175–184, Springer, Berlin, Germany, 2012. View at Google Scholar
  13. Y. Shi, M. Larson, and A. Hanjalic, “Mining contextual movie similarity with matrix factorization for context-aware recommendation,” ACM Transactions on Intelligent Systems and Technology, vol. 4, no. 1, article 16, 2013. View at Google Scholar
  14. L. Lü, M. Medob, C. Yeungb, Y. Zhang, Z. Zhang, and T. Zhou, “Recommender systems,” Physics Report, vol. 519, no. 1, pp. 1–49, 2012. View at Google Scholar
  15. A. Mislove, B. Viswanath, K. P. Gummadi, and P. Druschel, “You are who you know: inferring user profiles in online social networks,” in Proceedings of the ACM 3rd International Conference on Web Search and Data Mining (WSDM '10), pp. 251–260, New York, NY, USA, February 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Hu and Z. Gao, “Modules identification in gene positive networks of hepatocellular carcinoma using Pearson agglomerative method and Pearson cohesion coupling modularity,” Journal of Applied Mathematics, vol. 2012, Article ID 248658, 21 pages, 2012. View at Publisher · View at Google Scholar
  17. I. Cantador, P. Brusilovsky, and T. Kuflik, “Second Workshop on Information Heterogeneity and Fusion in Recommender Systems,” in Proceedings of the 5th ACM conference on Recommender Systems (RecSys '11), pp. 387–388, Chicago, Ill, USA, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. W. Nooy, A. Mrva, and V. Batagelj, Exploratory Social Network Analysis with Pajek, Cambridge University Press, Cambridge, UK, 2005.