THE HIERARCHICAL MODEL FOR NEWS RECOMMENDATION

Wenxing Hong, NanNan Zheng, Lei Wu, Youchun Ji, and Yang Weng

References

  1. [1] F. Ricci, L. Rokach, and B. Shapira, Introduction to recommender systems handbook, in Recommender systems handbook (Boston, MA: Springer, 2011), 1–35.
  2. [2] H. Li, S. Zhang, J. Shi, and Y. Hu, Research and design of intelligent learning system based on recommendation technology, Mechatronic Systems and Control, 47(1), 2019, 43–49.
  3. [3] S. Zhang, L. Yao, A. Sun, and Y. Tay, Deep learning based recommender system: A survey and new perspectives, ACM Computing Surveys (CSUR), 52(1), 2019, 5.
  4. [4] R. Baeza-Yates and B. Ribeiro-Neto, Modern information retrieval (New York, Harlow, England: ACM Press; Addison-Wesley, 2011).
  5. [5] G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin, Incorporating contextual information in recommender systems using a multidimensional approach, ACM Transactions on Information Systems (TOIS), 23(1), 2005, 103–145.
  6. [6] D. Goldberg, D. Nichols, B.M. Oki, and D. Terry, Using collaborative filtering to weave an information tapestry, Communications of the ACM, 35(12), 1992, 61–71.
  7. [7] W. Hong, S. Zheng, and H. Wang, Dynamic recommendation in e-recruitment system, Control and Intelligent Systems, 42(1), 2014, 3–8.
  8. [8] G. Linden, B. Smith, and J. York, Amazon.com recommendations: Item-to-item collaborative filtering, IEEE Internet computing, 2003(1), 2003, 76–80.
  9. [9] G.H. Golub and C. Reinsch, Singular value decomposition and least squares solutions, in Linear algebra (Berlin, Heidelberg: Springer, 1971), 134–151.
  10. [10] Y. Koren, Factorization meets the neighborhood: A multi-faceted collaborative filtering model, Proc. of the 14th ACM SIGKDD International Conf. on Knowledge Discovery and Data Mining, Las Vegas, 2008, 426–434.
  11. [11] Y. Koren, R. Bell, and C. Volinsky, Matrix factorization techniques for recommender systems, Computer, 2009(8), 2009, 30–37.
  12. [12] Y. Zhu, X. Shen, and C. Ye, Personalized prediction and sparsity pursuit in latent factor models, Journal of the American Statistical Association, 111(513), 2016, 241–252.
  13. [13] O. Ozgobek, J.A. Gulla, and R.C. Erdur, A survey on challenges and methods in news recommendation, Barcelona, WEBIST (2), 2014, 278–285.
  14. [14] M. Karimi, D. Jannach, and M. Jugovac, News recommender systems–survey and roads ahead, Information Processing & Management, 54(6), 2018, 1203–1227.
  15. [15] S. Okura, Y. Tagami, S. Ono, and A. Tajima, Embedding-based news recommendation for millions of users, Proc. of the 23rd ACM SIGKDD International Conf. on Knowledge Discovery and Data Mining, ACM, Halifax, 2017, 1933–1942.
  16. [16] V. Kumar, D. Khattar, S. Gupta, M. Gupta, and V. Varma, Deep neural architecture for news recommendation, CLEF (Working Notes), Dublin, 2017.
  17. [17] C. Chen, X. Meng, Z. Xu, and T. Lukasiewicz, Location-aware personalized news recommendation with deep semantic analysis, IEEE Access, 5, 2017, 1624–1638.
  18. [18] X. Bai, B.B. Cambazoglu, F. Gullo, A. Mantrach, and F. Silvestri, Exploiting search history of users for news personalization, Information Sciences, 385, 2017, 125–137.
  19. [19] G. Zheng, F. Zhang, Z. Zheng, et al., DRN: A deep reinforcement learning framework for news recommendation, Proc. of the 2018 World Wide Web Conf. on World Wide Web, International World Wide Web Conferences Steering Committee, Lyon, 2018, 167–176.
  20. [20] H. Wang, F. Zhang, X. Xie, and M. Guo, DKN: Deep knowledge-aware network for news recommendation, Proc. of the 2018 World Wide Web Conf. on World Wide Web, International World Wide Web Conferences Steering Committee, Lyon, 2018, 1835–1844.
  21. [21] J. Lian, F. Zhang, X. Xie, and G. Sun, Towards better representation learning for personalized news recommendation: A multi-channel deep fusion approach, IJCAI, Stockholm, 2018, 3805–3811.
  22. [22] J.D. Hamilton, Time series analysis, in Economic theory. II (USA: Princeton University Press), 1995, 625–630.
  23. [23] T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, 43(1), 1982, 59–69.
  24. [24] R. Tibshirani, Regression shrinkage and selection via the lasso: a retrospective, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(3), 2011, 273–282.
  25. [25] T. Sapatinas, The elements of statistical learning, Journal of the Royal Statistical Society: Series A (Statistics in Society), 167(1), 2004, 192–192.
  26. [26] H. Zou and T. Hastie, Addendum: regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(5), 2005, 768– 768.
  27. [27] D.M. Blei, A.Y. Ng, and M.I. Jordan, Latent Dirichlet allocation, Journal of Machine Learning Research, 3(Jan), 2003, 993–1022.
  28. [28] D.M. Blei and J.D. Lafferty, A correlated topic model of science, The Annals of Applied Statistics, 1(1), 2007, 17–35.
  29. [29] H.M. Wallach, Topic modeling: Beyond bag-of-words, Proc. of the 23rd International Conf. on Machine Learning, ACM, New York, 2006, 977–984.

Important Links:

Go Back