1.
2. Department of Economic Management, Wuhan University of Technology Huaxia College, Wuhan Hubei 430223, China
3. School of Economics, Wuhan University of Technology, Wuhan Hubei 430070, China
Abstract:To cope with the low accuracy of the mining results in the existing community discovery algorithms and the low quality of intelligent recommendation in the Web services resource, on the basis of the conventional collaborative filtering algorithms, a dynamic community discovery algorithm was proposed based on the nodes' similarity. Firstly, the central node that had the most connected nodes was regarded as the initial network community, and the community contribution degree was taken as the metric to continuously form a plurality of global saturated contribution degree communities. Then, an overlapping calculation was used to merge the communities of high similarity. Finally, the calculated results were arranged in descending order to form neighboring user sets for obtaining community recommendation object by calculating the dynamic similarity between target user and other users in the community. The experimental results show that the user social network community classification by the proposed community discovery algorithms is consistent with the real community classification results. The proposed algorithm can improve the accuracy of the community mining and helps to achieve high-quality community recommendation.
吴钟 聂规划 陈冬林 章佩璐. 基于协同过滤的Web服务动态社区发现算[J]. 计算机应用, 2013, 33(08): 2095-2099.
Zhong WU Gui-hua NIE CHEN Dong-lin ZHANG Peilu. Dynamic community discovery algorithm of Web services based on collaborative filtering. Journal of Computer Applications, 2013, 33(08): 2095-2099.
Zibin Zheng, Hao Ma, Michael R. Lyu,, Irwin King. QoS-Aware Web Service Recommendation by Collaborative Filtering [J]. IEEE Transactions on Services Computing, 2011,4(2): 140-152.
[5]
Qi Liu, Enhong Chen, Hui Xiong, Chris H. Q. Ding, Jian Chen. Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2012, 42(1): 218-232.
[6]
Hyeong-Joon Kwon, Kwang-Seok Hong. Personalized Smart TV Program Recommender Based on Collaborative Filtering and a Novel Similarity Method[J]. IEEE Transactions on Consumer Electronics, 2011,57(3):1416-1423.
Jian Wu, LiangChen, Yipeng Feng, Zibin Zheng, Meng Chu Zhou, Zhaohui Wu. Predicting Quality of Service for Selection by Neighborhood-Based Collaborative Filtering[J]. IEEE on Systems, Man, and Cybernetics: Systems, 2013, 43(2): 428-439.
[9]
Soo Ling Lim, Anthony Finkelstein. StakeRare: Using Social Networks and Collaborative Filtering for Large-Scale Requirements Elicitation[J]. IEEE Transactions on Software Engineering,2012, 38(3):707-735.