Create New Account
Login
Search or Buy Articles
Browse Journals
Browse Proceedings
Subscriptions
Submit your Paper
Submission Information
Journal Review
Recommend to Your Library
Call for Papers
The Hierarchical Model for News Recommendation
Wenxing Hong, NanNan Zheng, Lei Wu, Youchun Ji, Yang Weng
References
[1] F. Ricci, L. Rokach, and B. Shapira, “Introduction to recommender systems handbook,” inRecommender systems handbook. Springer, 2011, pp. 1–35.
[2] H. Li, S. Zhang, J. Shi, and Y. Hu, “Research and design of intelligent learning system basedon recommendation technology,” MECHATRONIC SYSTEMS AND CONTROL, vol. 47,no. 1, pp. 43–49, 2019.
[3] S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: A surveyand new perspectives,” ACM Computing Surveys (CSUR), vol. 52, no. 1, p. 5, 2019.
[4] R. Baeza-Yates, B. d. A. N. Ribeiro et al., Modern information retrieval. New York: ACMPress; Harlow, England: Addison-Wesley, 2011.
[5] G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin, “Incorporating contextual informationin recommender systems using a multidimensional approach,” ACM Transactionson Information Systems (TOIS), vol. 23, no. 1, pp. 103–145, 2005.
[6] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using collaborative filtering to weave aninformation tapestry,” Communications of the ACM, vol. 35, no. 12, pp. 61–71, 1992.
[7] W. Hong, S. Zheng, and H. Wang, “Dynamic recommendation in e-recruitment system,”Control and Intelligent Systems, vol. 42, no. 1, 2014.
[8] G. Linden, B. Smith, and J. York, “Amazon. com recommendations: Item-to-item collaborativefiltering,” IEEE Internet computing, no. 1, pp. 76–80, 2003.
[9] G. H. Golub and C. Reinsch, “Singular value decomposition and least squares solutions,” inLinear Algebra. Springer, 1971, pp. 134–151.
[10] Y. Koren, “Factorization meets the neighborhood: a multifaceted collaborative filtering model,”in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discoveryand data mining. ACM, 2008, pp. 426–434.
[11] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,”Computer, no. 8, pp. 30–37, 2009.
[12] Y. Zhu, X. Shen, and C. Ye, “Personalized prediction and sparsity pursuit in latent factormodels,” Journal of the American Statistical Association, vol. 111, no. 513, pp. 241–252,2016.
[13] O¨ . O¨ zgo¨bek, J. A. Gulla, and R. C. Erdur, “A survey on challenges and methods in newsrecommendation.” in WEBIST (2), 2014, pp. 278–285.
[14] M. Karimi, D. Jannach, and M. Jugovac, “News recommender systems–survey and roadsahead,” Information Processing & Management, vol. 54, no. 6, pp. 1203–1227, 2018.
[15] S. Okura, Y. Tagami, S. Ono, and A. Tajima, “Embedding-based news recommendation formillions of users,” in Proceedings of the 23rd ACM SIGKDD International Conference onKnowledge Discovery and Data Mining. ACM, 2017, pp. 1933–1942.
[16] V. Kumar, D. Khattar, S. Gupta, M. Gupta, and V. Varma, “Deep neural architecture for newsrecommendation.” in CLEF (Working Notes), 2017.
[17] C. Chen, X. Meng, Z. Xu, and T. Lukasiewicz, “Location-aware personalized news recommendationwith deep semantic analysis,” IEEE Access, vol. 5, pp. 1624–1638, 2017.
[18] X. Bai, B. B. Cambazoglu, F. Gullo, A. Mantrach, and F. Silvestri, “Exploiting search historyof users for news personalization,” Information Sciences, vol. 385, pp. 125–137, 2017.
[19] G. Zheng, F. Zhang, Z. Zheng, Y. Xiang, N. J. Yuan, X. Xie, and Z. Li, “Drn: A deep reinforcementlearning framework for news recommendation,” in Proceedings of the 2018 WorldWide Web Conference on World Wide Web. International World Wide Web ConferencesSteering Committee, 2018, pp. 167–176.
[20] H. Wang, F. Zhang, X. Xie, and M. Guo, “Dkn: Deep knowledge-aware network for newsrecommendation,” in Proceedings of the 2018 World Wide Web Conference on World WideWeb. International World Wide Web Conferences Steering Committee, 2018, pp. 1835–1844.
[21] J. Lian, F. Zhang, X. Xie, and G. Sun, “Towards better representation learning for personalizednews recommendation: a multi-channel deep fusion approach.” in IJCAI, 2018, pp.3805–3811.
[22] J. D. Hamilton, “Time series analysis,” Economic Theory. II, Princeton University Press,USA, pp. 625–630, 1995.
[23] T. Kohonen, “Self-organized formation of topologically correct feature maps,” Biologicalcybernetics, vol. 43, no. 1, pp. 59–69, 1982.
[24] R. Tibshirani, “Regression shrinkage and selection via the lasso: a retrospective,” Journal ofthe Royal Statistical Society: Series B (Statistical Methodology), vol. 73, no. 3, pp. 273–282,2011.
[25] T. Sapatinas, “The elements of statistical learning,” Journal of the Royal Statistical Society:Series A (Statistics in Society), vol. 167, no. 1, pp. 192–192, 2004.
[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), vol. 67, no. 5,pp. 768–768, 2005.
[27] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of machineLearning research, vol. 3, no. Jan, pp. 993–1022, 2003.
[28] D. M. Blei, J. D. Lafferty et al., “A correlated topic model of science,” The Annals of AppliedStatistics, vol. 1, no. 1, pp. 17–35, 2007.
[29] H. M.Wallach, “Topic modeling: beyond bag-of-words,” in Proceedings of the 23rd internationalconference on Machine learning. ACM, 2006, pp. 977–984.
Important Links:
Abstract
DOI:
10.2316/J.2019.201-0062
From Journal
(201) Mechatronic Systems and Control - 2019
Go Back