UBS: A Novel News Recommendation System Based on User Behavior Sequence

1. Liu, J., Dolan, P., Pedersen, E.: Personalized news recommendation based on click behavior. In: Intelligent User Interfaces, pp. 31–40 (2010)

   Google Scholar 

2. Li, L., Wang, D., Zhu, S., Li, T.: Personalized news recommendation: A review and an experimental investigation. Journal of Computer Science and Technology 26(5), 754–766 (2011)

   Article  Google Scholar 

3. Das, A.S., Datar, M., Garg, A., et al.: Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th International Conference on World Wide Web (2007)

   Google Scholar 

4. Marung, U., Theera-Umpon, N., Auephanwiriyakul, S.: Applying memetic algorithm-based clustering to recommender system with high sparsity problem. Journal of Central South University September 21(9), 3541–3550 (2014)

   Article  Google Scholar 

5. Gao, H., Tang, J., Liu, H.: Addressing the cold-start problem in location recommendation using geo-social correlations. Data Mining and Knowledge Discovery (2014)

   Google Scholar 

6. Kavitha Devi, M.K., Venkatesh, P.: Smoothing approach to alleviate the meager rating problem in collaborative recommender systems. Future Generation Computer Systems 29, 262–270 (2013)

   Article  Google Scholar 

7. Wanaskar, U., Sheetal, R., Mukhopadhyay, D.: A Hybrid Web Recommendation System Based on the Improved Association Rule Mining Algorithm. Journal of Software Engineering and Applications, 396–404 (2013)

   Google Scholar 

8. Li, D., Lv, Q., Xie, X., Li, S., et al.: Interest-based real-time content recommendation in online social communities. Knowledge-Based Systems 28, 1–12 (2012)

   Article  Google Scholar 

9. Sergio, C., Juan, M., Juan, F.: Top-N news recommendations in digital newspapers. Knowledge-Based Systems 27, 180–189 (2012)

   Article  Google Scholar 

10. Wen, H., Fang, L., Guan, L.: A hybrid approach for personalized recommendation of news on the Web. Expert Systems with Applications 39, 5806–5814 (2012)

    Article  Google Scholar 

11. Garcin, F., Zhou, K., Faltings, B., Schickel, V.: Personalized news recommendation based on collaborative filtering. In: IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (2012)

    Google Scholar 

12. Wang, J., Vries, A.P., Reinders, M.J.T.: Unifying user-based item-based collaborative filtering approaches by similarity fusion. In: SIGIR 2006 Proceedings of the 29th annual international ACM SIGIR conference on Research and Development in Information Retrieval, pp. 501–508 (2006)

    Google Scholar 

13. Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Kobsa, A., Nejdl, W., Brusilovsky, P. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

14. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)

    Google Scholar 

15. Li, L., Wang, D., Li, T., Knox, D., Padmanabhan, B.: 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 2011), pp. 125–134 (2011)

    Google Scholar 

16. Li, L., Li, T.: News recommendation via hypergraph learning: encapsulation of user behavior and news content. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (WSDM 2013), pp. 305–314 (2013)

    Google Scholar 

17. Bai, X., Chen, F., Zhan, S.: A New Clustering Model Based on Word2vec Mining on Sina Weibo Users Tags. International Journal of Grid Distribution Computing 7, 41–48 (2014)

    Article  Google Scholar 

18. Yuan, Y., He, L., Peng, L., Huang, Z.: A New Study Based on Word2vec and Cluster for Document Categorization. Journal of Computational Information Systems 10, 9301–9308 (2014)

    Google Scholar 

19. Xin, Z., Zhao, J., Gu, M., Sun, J.: Integrating collaborate and content-based filtering for personalized information recommendation. In: Hao, Y., Liu, J., Wang, Y., Cheung, Y., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS, vol. 3801, pp. 476–482. Springer, Berlin Heidelberg (2005)

    Chapter  Google Scholar 

20. Goto, M., Komatani, K., et al.: Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In: Proceedings of the 7th International Conference on Music Information Retrieval, pp. 296–301 (2006)

    Google Scholar 

21. Hu, Z., Wang, H., Zhu, J., Li, M., Qiao, Y., Deng, C.: Discovery of rare sequential topic patterns in document stream. In: Proc. Siam International Conference On Data Mining 2014, pp. 533–541 (2014)

    Google Scholar 

22. Guan, Y., Cai, S., Shang, M.: Recommendation algorithm based on item quality and user rating preferences. Frontiers of Computer Science 8(2), 289–297 (2014)

    Article  MathSciNet  Google Scholar 

23. Li, L., Zheng, L., Yang, F., et al.: Modeling and broadening temporal user interest in personalized news recommendation. Expert Systems with Applications 41(7), 3168–3177 (2014)

    Article  Google Scholar 

24. Lin, C., Xie, R., Guan, X., et al.: Personalized news recommendation via implicit social experts. Information Sciences 254, 1–18 (2014)

    Article  Google Scholar 

25. Garcin F., Dimitrakakis C., Faltings B.: Personalized news recommendation with context trees. In: 7th ACM Recommender Systems Conference (Recsys 2013), pp. 105–112 (2013)

    Google Scholar 

26. Lei, L., Tao, L.: News recommendation via hypergraph learning: encapsulation of user behavior and news content. In: WSDM 2013. ACM (2013)

    Google Scholar 

Download references