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The Scientific World Journal
Volume 2014, Article ID 907515, 14 pages
http://dx.doi.org/10.1155/2014/907515
Research Article

An Effective News Recommendation Method for Microblog User

School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China

Received 4 December 2013; Accepted 19 February 2014; Published 2 April 2014

Academic Editors: Z. Chen and F. Yu

Copyright © 2014 Wanrong Gu 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. O. Phelan, K. McCarthy, and B. Smyth, “Using twitter to recommend real-time topical news,” in Proceedings of the 3rd ACM Conference on Recommender Systems, pp. 385–388, ACM, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. G. de Francisci Morales, A. Gionis, and C. Lucchese, “From chatter to headlines: Harnessing the real-time web for personalized news recommendation,” in Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM '12), pp. 153–162, ACM, February 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. L. Li, D. Wang, T. Li, D. Knox, and B. Padmanabhan, “SCENE: A scalable two-stage personalized news recommendation system,” in Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '11), pp. 125–134, July 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Liu, P. Dolan, and E. R. Pedersen, “Personalized news recommendation based on click behavior,” in Proceedings of the 15th ACM International Conference on Intelligent User Interfaces (IUI '10), pp. 31–40, ACM, February 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Schafer, J. Konstan, and J. Riedi, “Recommender systems in e-commerce,” in Proceedings of the 1st ACM conference on Electronic commerce, pp. 158–166, ACM, 1999.
  6. D. Billsus and M. J. Pazzani, “Personal news agent that talks, learns and explains,” in Proceedings of the 3rd Annual Conference on Autonomous Agents, pp. 268–275, ACM, May 1999. View at Scopus
  7. M. Pazzani and D. Billsus, “The identification of interesting web sites,” Machine Learning, vol. 27, no. 3, pp. 313–331, 1997. View at Google Scholar · View at Scopus
  8. U. Shardanand and P. Maes, “Social information filtering: algorithms for automating ‘word of mouth’,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 210–217, ACM, May 1995. View at Scopus
  9. R. G. Cota, A. A. Ferreira, C. Nascimento, M. A. Gonçalves, and A. H. F. Laender, “An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations,” Journal of the American Society for Information Science and Technology, vol. 61, no. 9, pp. 1853–1870, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” in Proceedings of the 14th conference on Uncertainty in Artificial Intelligence, pp. 43–52, Morgan Kaufmann Publishers, 1998.
  11. A. S. Das, M. Datar, A. Garg, and S. Rajaram, “Google news personalization: Scalable online collaborative filtering,” in Proceedings of the 16th International World Wide Web Conference (WWW '07), pp. 271–280, ACM, May 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Balabanović and Y. Shoham, “Content-Based, Collaborative Recommendation,” Communications of the ACM, vol. 40, no. 3, pp. 66–72, 1997. View at Google Scholar · View at Scopus
  13. M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin, “Combining content-based and collaborative filters in an online newspaper,” in Proceedings of the ACM SIGIR Workshop on Recommender Systems, vol. 60, Citeseer, 1999.
  14. W. Chu and S. T. Park, “Personalized recommendation on dynamic content using predictive bilinear models,” in Proceedings of the 18th International Conference on World Wide Web, pp. 691–700, ACM, 2009.
  15. L. Li, W. Chu, J. Langford, and R. E. Schapire, “A contextual-bandit approach to personalized news article recommendation,” in Proceedings of the 19th International Conference World Wide Web (WWW '10), pp. 661–670, ACM, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. C. Best, E. van der Goot, M. de Paola, T. Garcia, and D. Horby, “Europe media monitor—emm,” JRC Technical Note no. I, 2, 2002.
  17. E. Gabrilovich, S. Dumais, and E. Horvitz, “Newsjunkie: providing personalized newsfeeds via analysis of information novelty,” in Proceedings of the 13th International conference World Wide Web (WWW '04), pp. 482–490, ACM, May 2004. View at Scopus
  18. G. Wang, F. H. Lochovsky, and Q. Yang, “Feature selection with conditional mutual information MaxiMin in text categorization,” in Proceedings of the 13th ACM Conference on Information and Knowledge Management (CIKM '04), pp. 342–349, ACM, November 2004. View at Scopus
  19. C. Lee and G. G. Lee, “Information gain and divergence-based feature selection for machine learning-based text categorization,” Information Processing and Management, vol. 42, no. 1, pp. 155–165, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. A. M. A. Mesleh, “Chi square feature extraction based svms arabic language text categorization system,” Journal of Computer Science, vol. 3, no. 6, pp. 430–435, 2007. View at Google Scholar
  21. Z. Wei, D. Miao, J. H. Chauchat, and C. Zhong, “Feature selection on chinese text classification using character N-grams,” in Rough Sets and Knowledge Technology, vol. 5009 of Lecture Notes in Computer Science, pp. 500–507, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. S. Lai and C. H. Wu, “Meaningful term extraction and discriminative term selection in text categorization via unknown-word methodology,” ACM Transactions on Asian Language Information Processing (TALIP), vol. 1, no. 1, pp. 34–64, 2002. View at Google Scholar
  23. R. Rifkin and A. Klautau, “In defense of one-vs-all classification,” The Journal of Machine Learning Research, vol. 5, pp. 101–141, 2004. View at Google Scholar
  24. A. E. Hoerl and R. W. Kennard, “Ridge regression: Biased estimation for nonorthogonal problems,” Technometrics, vol. 42, no. 1, pp. 80–86, 2000. View at Google Scholar · View at Scopus
  25. F. Wang and C. Zhang, “Feature extraction by maximizing the average neighborhood margin,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), June 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. X. Zhang, W. Wang, K. Nørvåg, and M. Sebag, “K-AP: generating specified K clusters by efficient affinity propagation,” in Proceedings 10th IEEE International Conference on Data Mining (ICDM '10), pp. 1187–1192, IEEE, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. J. D. Hamilton, Time Series Analysis, vol. 2, Cambridge University Press, Cambridge, UK, 1994.
  28. F. Abel, Q. Gao, G.-J. Houben, and K. Tao, “Analyzing user modeling on Twitter for personalized news recommendations,” in User Modeling, Adaption and Personalization, vol. 6787 of Lecture Notes in Computer Science, pp. 1–12, Springer, Berlin, Germany, 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. S. Gauch, M. Speretta, A. Chandramouli, and A. Micarelli, “User profiles for personalized information access,” The Adaptive Web, Springer, Berlin, Germany, vol. 4321, pp. 54–89, 2007. View at Google Scholar · View at Scopus
  30. G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher, “An analysis of approximations for maximizing submodular set functions-I,” Mathematical Programming, vol. 14, no. 1, pp. 265–294, 1978. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Khuller, A. Moss, and J. Naor, “The budgeted maximum coverage problem,” Information Processing Letters, vol. 70, no. 1, pp. 39–45, 1999. View at Google Scholar · View at Scopus
  32. J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. Vanbriesen, and N. Glance, “Cost-effective outbreak detection in networks,” in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429, ACM, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  33. X. Cheng, S. Tan, and L. Tang, “Using dragpushing to refine concept index for text categorization,” Journal of Computer Science and Technology, vol. 21, no. 4, pp. 592–596, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. Z. Guo, L. Lu, S. Xi, and F. Sun, “An effective dimension reduction approach to chinese document classification using genetic algorithm,” in Advances in Neural Networks—ISNN 2009, vol. 5552 of Lecture Notes in Computer Science, pp. 480–489, Springer, Berlin, Germany, 2009. View at Google Scholar
  35. H. Cunningham, D. Maynard, K. Bontcheva, and V. Tablan, “Gate: an architecture for development of robust hlt applications,” in Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 168–175, Association for Computational Linguistics, 2002.