稀疏分布波長轉(zhuǎn)換的光突發(fā)交換網(wǎng)絡(luò)路由優(yōu)化問題研究

1、Department of AutomationXiamen UniversityYouchun Ji, Wenxing Hong*, Jianwei QiNovember, 2015,Missing Value Prediction Using Co-clustering and RBF for Collaborative Filtering,,1,Case,i.xmrc.com.cn,Website,,Interest,550

2、1.cn,17du.info,Job recommendation,Expert finding,News recommendation,2012-2014,2014-now,2014-now,2,Outline,,Introduction,,Algorithms & Experiments,,The Problem Definition,3,Introduction,Jannach, D., M. Zanker, A. Fel

3、fernig, &G. Friedrich, Recommender systems: an introduction. 2010: Cambridge University Press.Zheng, L., L. Li, W. Hong, &T. Li, PENETRATE: Personalized news recommendation using ensemble hierarchical clustering

4、. Expert Systems with Applications, 2013. 40(6): p. 2127-2136.Das, A.S., M. Datar, A. Garg, &S. Rajaram. Google news personalization: scalable online collaborative filtering. in Proceedings of the 16th international

5、 conference on World Wide Web. 2007. ACM.Breese, J.S., D. Heckerman, &C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. in Proceedings of the Fourteenth conference on Uncertainty in

6、artificial intelligence. 1998. Morgan Kaufmann Publishers Inc.,Help users find interesting articles that match the users’ preference as much as possible.,4,Introduction,,,,Collaborative filtering is one of the most succe

7、ssful methods for news recommendation systems.,Pazzani, M.J., A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 1999. 13(5-6): p. 393-408.Huang, Z., H. Chen, &D.

8、 Zeng, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems (TOIS), 2004. 22(1): p. 116-142.Hofmann, T., Latent semantic models

9、for collaborative filtering. ACM Transactions on Information Systems (TOIS), 2004. 22(1): p. 89-115.Blei, D.M., A.Y. Ng, &M.I. Jordan, Latent dirichlet allocation. The Journal of machine Learning research, 2003. 3:

10、p. 993-1022.,5,Motivation,Zhang, S., W. Wang, J. Ford, &F. Makedon. Learning from Incomplete Ratings Using Non-negative Matrix Factorization. in SDM. 2006. SIAM.Dhillon, I.S. Co-clustering documents and words using

11、bipartite spectral graph partitioning. in Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. 2001. ACM.,The sparsity of user-item rating matrix will lead to the negativ

12、e effect of collaborative filtering algorithm.,The number of news which users have read is far less than the news published on the website.,6,Outline,,Introduction,,Algorithms & Experiments,,The Problem Definition,7,

13、The Problem Definition,,,,,,George, T., &S. Merugu. A scalable collaborative filtering framework based on co-clustering. in Data Mining, Fifth IEEE International Conference on. 2005. IEEE.,,,,8,Outline,,Introduction,

14、,Algorithms & Experiments,,The Problem Definition,9,Data sample – Reading History,,,,News ID,User ID,10,Algorithms – Flow chart,,,,,,,,11,Algorithms – Co-clustering,,,,,,,,,0.8756,,,0.7535,0.9400,,0.2241,,1. Hu, W.,

15、W. Yong-Ji, W. Zhe, W. Xiu-Li, et al., Two-Phase Collaborative Filtering Algorithm Based on Co-Clustering. Journal of Software. 21: p. 1042-1054 (in Chinese).2. George, T., &S. Merugu. A scalable collaborative filte

16、ring framework based on co-clustering. in Data Mining, Fifth IEEE International Conference on. 2005. IEEE.3. Dhillon, I.S. Co-clustering documents and words using bipartite spectral graph partitioning. in Proceedings of

17、 the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. 2001. ACM.,12,Algorithms - RBF,,,,,,,,1. https://en.wikipedia.org/wiki/Radial_basis_function_network.2. Fuliang, X., &Z. Huiyi

18、ng, A Research of Collaborative Filtering Recommender MethodBased on SOM and RBFN Filling Missing Values. XIANDAI TUSHU QINGBAO JISHU, 2014. 7/8: p. 56-63 (in Chinese).,13,Platform - http://yiqidu.xmu.edu.cn/,14,Data set

19、 – XMU News,The experiment data comes from Xiamen University news reading website which is focus on campus news. It includes 9502 users, 6372 news and 932640 rating. The sparseness of the user-item rating matrix is 98.46

20、%. The data set was divided into testing set and training sets.,Rating:: UserID:: NewsID:: News title,1. Jiang, S., &W. Hong. A vertical news recommendation system: CCNS—An example from Chinese campus news reading sy

21、stem. in Computer Science & Education (ICCSE), 2014 9th International Conference on. 2014. IEEE.,15,Experiments,,,,,,,,The number of co-clustering is 36 in the experiment. After prediction the missing values, the spa

22、rseness of the user-item rating matrix reduce to about 60%.,Before>0.95,After<0.65,,co- clustering number,sparseness,16,Experiments,,,,,,,,As the experiment result shows, the prediction method that combine c

23、o-clustering and RBF work effective on XMUNEWS data set. The root mean square error is 1.553.,,17,Outline,,Introduction,,Algorithms & Experiments,,The Problem Definition,18,Conclusion,,,,Before prediction, the sparse

24、ness of the user-item rating matrix is above 96%. But after prediction, it reduce to below 60%.The root mean square error of true rating values and prediction rating values is 1.553 on XMUNEWS data set. As the experimen

25、t result shows, the combining algorithm is better than the separate algorithm.We built an online website to collect data and do experiments (http://yiqidu.xmu.edu.cn/).For future work, we will concentrate on how to imp

26、rove the computational efficiency and how to choose the number of clusters.,19,References,,Jannach, D., M. Zanker, A. Felfernig, &G. Friedrich, Recommender systems: an introduction. 2010: Cambridge University Press.

27、Zheng, L., L. Li, W. Hong, &T. Li, PENETRATE: Personalized news recommendation using ensemble hierarchical clustering. Expert Systems with Applications, 2013. 40(6): p. 2127-2136.Das, A.S., M. Datar, A. Garg, &S

28、. Rajaram. Google news personalization: scalable online collaborative filtering. in Proceedings of the 16th international conference on World Wide Web. 2007. ACM.Breese, J.S., D. Heckerman, &C. Kadie. Empirical anal

29、ysis of predictive algorithms for collaborative filtering. in Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. 1998. Morgan Kaufmann Publishers Inc.Pazzani, M.J., A framework for colla

30、borative, content-based and demographic filtering. Artificial Intelligence Review, 1999. 13(5-6): p. 393-408.Huang, Z., H. Chen, &D. Zeng, Applying associative retrieval techniques to alleviate the sparsity problem

31、in collaborative filtering. ACM Transactions on Information Systems (TOIS), 2004. 22(1): p. 116-142.Hofmann, T., Latent semantic models for collaborative filtering. ACM Transactions on Information Systems (TOIS), 2004.

32、22(1): p. 89-115.Blei, D.M., A.Y. Ng, &M.I. Jordan, Latent dirichlet allocation. The Journal of machine Learning research, 2003. 3: p. 993-1022.Zhang, S., W. Wang, J. Ford, &F. Makedon. Learning from Incomplete

33、 Ratings Using Non-negative Matrix Factorization. in SDM. 2006. SIAM.Dhillon, I.S. Co-clustering documents and words using bipartite spectral graph partitioning. in Proceedings of the seventh ACM SIGKDD international co

34、nference on Knowledge discovery and data mining. 2001. ACM.George, T., &S. Merugu. A scalable collaborative filtering framework based on co-clustering. in Data Mining, Fifth IEEE International Conference on. 2005. I

35、EEE.https://en.wikipedia.org/wiki/Radial_basis_function_network.Fuliang, X., &Z. Huiying, A Research of Collaborative Filtering Recommender MethodBased on SOM and RBFN Filling Missing Values. XIANDAI TUSHU QINGBAO

36、JISHU, 2014. 7/8: p. 56-63 (in Chinese).Jiang, S., &W. Hong. A vertical news recommendation system: CCNS—An example from Chinese campus news reading system. in Computer Science & Education (ICCSE), 2014 9th Inte

37、rnational Conference on. 2014. IEEE.,,20,Acknowledgment,The research was supported by the National Natural Science Foundation of China under Grant No.61303081 and by the Fundamental Research Funds for the Xiamen Universi