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浙江大学学报(工学版)
服务计算     
基于贝叶斯分类的Web服务质量预测方法研究
任迪, 万健, 殷昱煜, 周丽, 高敏
杭州电子科技大学 计算机学院,浙江 杭州 310018; 复杂系统建模与仿真教育部重点实验室,浙江 杭州 310018
Web services QoS prediction method based on Bayes classification
REN Di, WAN Jian, YIN Yu-yu, ZHOU Li, GAO Min
1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; 2. Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou 310018, China
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摘要:

针对网络环境不稳定导致Web服务质量(QoS)数据中存在噪声数据,进而降低Web服务质量预测精度的问题,提出一种基于贝叶斯分类的混合协同过滤Web服务质量值预测方法.该方法使用贝叶斯算法对Web服务质量数据进行分类并得到每个分类的概率,利用分类结果确定缺失值可能的取值范围,并对用户和服务的相似邻居进行过滤.通过引入分类概率,改进传统的协同过滤方法得到最终的缺失值预测结果,在一定程度上消除了噪声数据对Web服务质量预测的影响.实验结果表明:较之现有方法,该方法具有更好的预测精度.

Abstract: A novel hybrid collaborative filtering quality of service (QoS) prediction method based on Bayes classification was proposed in order to address the problem that network instability may lead to some noisy QoS data in real environment, and the utilization of noisy data would decrease the prediction accuracy greatly. This method first employed Bayes algorithm to classify Web service QoS data and compute the probability of every classification. Then the possible range of the missing QoS value could be identified. The similar neighbors were filtered according to the range. At last, the traditional collaborative filtering algorithm was improved to compute the final prediction results by using the probability of the classifications. To some extent, the proposed method can reduce the impact of the noisy data. Compared with the existing methods, the experimental results demonstrate that our method can achieve higher prediction accuracy.
出版日期: 2017-06-11
CLC:  TP 312  
基金资助:

国家科技支撑计划资助项目(2014BAK14B04);浙江省自然科学基金资助项目(LY16F020017);国家自然科学基金资助项目(61100043);浙江省自然科学基金资助项目(LY16F020017);中国博士后基金资助项目(2013M540492).

通讯作者: 殷昱煜,男,副教授. ORCID: 0000-0001-7565-4111.     E-mail: yinyuyu@hdu.edu.cn
作者简介: 任迪(1992—),男,硕士生,从事机器学习、推荐系统研究. ORCID: 0000-0002-0831-8975. E-mail: rendi_hdu@163.com
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引用本文:

任迪, 万健, 殷昱煜, 周丽, 高敏. 基于贝叶斯分类的Web服务质量预测方法研究[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2017.06.023.

REN Di, WAN Jian, YIN Yu-yu, ZHOU Li, GAO Min. Web services QoS prediction method based on Bayes classification. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2017.06.023.

参考文献(References):
[1] SHAO L, ZHANG J, Wei Y, et al. Personalized QoS prediction for Web services via collaborative filtering [C] ∥ 2007 IEEE International Conference on Web Services. Salt
Lake City: IEEE, 2007: 439-446.
[2] ZHENG Z, MA H, LYU M R, et al. QoS-aware Web service recommendation by collaborative filtering [J]. IEEE Transactions on Services Computing, 2011, 4(2): 140-152.
[3] KONSTAN J A, MILLER B N, MALTZ D, et al. GroupLens: applying collaborative filtering to Usenet news [J]. Communications of the ACM, 1997, 40(3): 77-87.
[4] RESNICK P, IACOVOU N, SUCHAK M, et al. GroupLens: an open architecture for collaborative filtering of netnews [C] ∥ Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work. New York: ACM, 1994: 175-186.
[5] LINDEN G, SMITH B, YORK J. Amazon. com recommendations: item-to-item collaborative filtering [J]. IEEE Internet computing, 2003, 7(1): 76-80.
[6] HILL W, STEAD L, ROSENSTEIN M, et al. Recommending and evaluating choices in a virtual community of use [C] ∥ Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM, 1995: 194-201.
[7] DESHPANDE M, KARYPIS G. Item-based top-n recommendation algorithms [J]. ACM Transactions on Information Systems (TOIS), 2004, 22(1): 143-177.
[8] BALABANOVI M, SHOHAM Y. Fab: content-based, collaborative recommendation [J].Communications of the ACM, 1997, 40(3): 66-72.
[9] STOJANOVA D, CECI M, APPICE A, et al. Network regression with predictive clustering trees [J]. Data Mining and Knowledge Discovery, 2012, 25(2): 378-413.
[10] MILLER B N, ALBERT I, LAM S K, et al. MovieLens unplugged: experiences with an occasionally connected recommender system [C] ∥ Proceedings of the 8th International
Conference on Intelligent User Interfaces. Florida: ACM, 2003: 263-266.
[11] SU X, KHOSHGOFTAAR T M. A survey of collaborative filtering techniques [J].Advances in Artificial Intelligence, 2009, 2009(4): 1-19.
[12] ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions [J]. IEEE Transactions on
Knowledge and Data Engineering, 2005, 17(6): 734-749.
[13] BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering [C] ∥ Proceedings of the Fourteenth Conference on Uncertainty
in Artificial Intelligence. Madison: Morgan Kaufmann Publishers Inc, 1998: 43-52.
[14] CARDELLINI V, CASALICCHIO E, GRASSI V, et al. Flowbased service selection for Web service composition supporting multiple QoS classes[C]∥2007 IEEE International Conference on Web Services. Salt Lake City: IEEE, 2007: 743-750.
[15] JIA Z, YANG Y, GAO W, et al. User-based collaborative filtering for tourist attraction recommendations\[C\]∥Computational Intelligence and Communication Technology
(CICT). Paris: IEEE, 2015: 22-25.
[16] SARWAR B, KARYPIS G, KONSTAN J, et al.Item-based collaborative filtering recommendation algorithms [C]∥Proceedings of the 10th International Conference on World
Wide Web. Hong Kong: ACM, 2001: 285-295.
[17] ZHENG Z, MA H, LYU M R, et al. Wsrec: a collaborative filtering based web service recommender system [C] ∥ 2009 IEEE International Conference on Web Services. Los
Angeles: IEEE, 2009: 437-444.
[18] SHAO L, ZHANG J, WEI Y, et al. Personalized QoS prediction for Web services via collaborative filtering [C] ∥ 2007 IEEE International Conference on Web Services. Salt
Lake City: IEEE, 2007: 439-446.
[19] ZHENG Z, ZHANG Y, LYU M R. Distributed QoS evaluation for real-world Web services [C] ∥ 2010 IEEE International Conference on Web Services. Miami: IEEE, 2010: 83-90.
[20] RENNIE J D M, SREBRO N. Fast maximum margin matrix factorization for collaborative prediction [C] ∥Proceedings of the 22nd International Conference on Machine Learning. Bonn: ACM, 2005: 713-719.
[21] SALAKHUTDINOV R, MNIH A. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo [C] ∥ Proceedings of the 25th International Conference on Machine Learning. Helsinki: ACM, 2008: 880-88.
[22] SALAKHUTDINOV R, MNIH A. Probabilistic matrix factorization [J]. Advances in Neural Information Processing Systems, 2007, 49(8): 1257-1264.
[23] LO W, YIN J, DENG S, et al. Collaborative Web service QoS prediction with location-based regularization [C] ∥ 2012 IEEE 19th International Conference on Web Services.
Honolulu: IEEE, 2012: 464-471.
[24] HE P, ZHU J, ZHENG Z, et al. Location-based hierarchical matrix factorization for Web service recommendation [C] ∥ 2014 IEEE International Conference on Web Services. Anchorage: IEEE, 2014: 297-304.
[25] XU Y, YIN J, LO W, et al. Personalized locationaware QoS prediction for Web services using probabilistic matrix factorization [C] ∥ 14th International Conference 
on Web Information Systems Engineering. Nanjing: WISE, 2013: 229-242.
[26] CHEN X, ZHENG Z, LIU X, et al. Personalized QoS-aware web service recommendation and visualization [J]. IEEE Transactions on Services Computing, 2013, 6(1): 35-47.
[27] CHEN X, LIU X, HUANG Z, et al. Regionknn: a scalable hybrid collaborative filtering algorithm for personalized Web service recommendation [C] ∥ IEEE International
Conference on Web Services. Miami: IEEE, 2010: 9-16.
[28] KOREN Y. Collaborative filtering with temporaldynamics [J]. Communications of the ACM, 2010,53(4): 89-97.
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