基于张量分解的动态Web服务推荐

张万才; 刘旭东; 郭晓辉

doi:10.13700/j.bh.1001-5965.2015.0582

基于张量分解的动态Web服务推荐

doi: 10.13700/j.bh.1001-5965.2015.0582

北京航空航天大学计算机学院, 北京 100083

基金项目: 国家自然科学基金（61370057）；国家“863”计划（2012AA011203）；国家“973”计划（2014CB340304）

详细信息

作者简介:
刘旭东,男,博士,教授,博士生导师。主要研究方向:服务计算、云计算。Tel.:010-82316285,E-mail:liuxd@act.buaa.edu.cn;张万才,男,博士研究生。主要研究方向:服务计算。E-mail:zhangwc@act.buaa.edu.cn

通讯作者:
刘旭东,Tel.:010-82316285,E-mail:liuxd@act.buaa.edu.cn

中图分类号: TP399
计量
- 文章访问数: 923
- HTML全文浏览量: 23
- PDF下载量: 764
- 被引次数: 0
出版历程
- 收稿日期: 2015-09-08
- 网络出版日期: 2016-09-20

Dynamic Web service recommendation based on tensor factorization

School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

Funds: National Natural Science Foundation of China (61370057); National High Technology Research and Development Program of China (2012AA011203); National Key Basic Research Program of China (2014CB340304)

摘要

摘要: 在服务计算领域中，为了能够在大量具有相同功能的Web服务以及API等数据资源中选择适合用户的服务和接口，提出了服务推荐系统。当前常用的基于服务质量（QoS）的服务推荐系统所采用的模型假定服务的QoS值恒定不变，是一种由服务和用户的二元关系构成的二维静态模型。针对实际应用中，QoS是受到多种因素影响的变量这一问题，提出了一种可以描述多个影响QoS因素的张量模型，并利用张量分解算法来对服务推荐算法进行了改进。实验结果表明：提出的基于张量分解的服务推荐算法与6种现有算法相比较，预测服务的QoS值的绝对平均误差（MAE）不同程度地降低了20%~50%，并且所建模型能够描述更多的影响因素，从而可对服务进行动态推荐。
- 服务计算 /
- 服务质量 /
- 推荐系统 /
- 协同过滤 /
- 张量分解
Abstract: In the area of Web service computing, in order to select a suitable service for users in a large number of Web services and API with the identical function，the issue of Web service recommendation is becoming more and more critical. At present, in the quality of service (QoS) based service recommendation systems, the hypothesis of the system model is a two-dimensional static model which is composed of dyadic relationship between users and service interaction. However, in view of the practical application, the QoS value is affected by many factors, and a tensor model is proposed to describe the factors which affect the QoS. Then, we propose a method to discover the latent factors that govern the associations among these multi-type objects of QoS. A new recommendation approach based on tensor factorization is proposed to address the issue of Web service QoS value prediction with considering Web service invocation time. The experimental results show that compared with six related algorithms, the mean absolute error (MAE) of the proposed tensor factorization algorithm is reduced by 20%-50%, and our model can be used to describe more factors and to dynamically recommend Web service.
- service computing /
- quality of service /
- recommendation systems /
- collaborative filtering /
- tensor factorization

HTML全文

参考文献(24)

[1]	STRUNK A.QoS-aware service composition:A survey[C]//Proceedings of 8th IEEE European Conference on Web Services(ECOWS).Piscataway,NJ:IEEE Press,2010:67-74.
[2]	HADDAD J,MANOUVRIER M,RUKOZ M.TQoS:Transactional and QoS-aware selection algorithm for automatic Web service composition[J].IEEE Transactions on Services Computing,2010,3(1):73-85.
[3]	AI-MASRI E,MAHMOUD Q H.Identifying client goals for Web service discovery[C]//IEEE International Conference on Services Computing,2009.Piscataway,NJ:IEEE Press,2009:202-209.
[4]	ZHENG Z B,MA H,LYU M R,et al.WSRec:A collaborative filtering based Web service recommender system[C]//Internationnal Conference on Web Services.Piscataway,NJ:IEEE Press,2009:437-444.
[5]	SUNG H H.Helping online customers decide through Web personalization[J].IEEE Intelligent Systems,2005,17(6):34-43.
[6]	ZHENG Z,MA H,LYU M R,et al.QoS-aware Web service recommendation by collaborative filtering [J].Services Computing,2011,4(2):140-152.
[7]	XIONG L,CHEN X,HUANG T K,et al.Temporal collaborative filtering with Bayesian probabilistic tensor factorization [C]// Proceedings of the 10th SIAM International Conference on Data Mining,SDM 2010.Philadelphia:Society for Industrial and Applied Mathematics,2010:211-222.
[8]	TUCKER L R.Some mathematical notes on three-mode factor analysis[J].Psychometrika,1966,31(3):279-311.
[9]	HARSHMAN R A.Foundations of the PARAFAC procedure:Models and conditions for an "explanatory" multi-model factor analysis[J].UCLA Working Papers in Phonetics,1970(16):1-84.
[10]	KOLDA T G,BADER B W.Tensor decompositions and applications[J].SIAM Review,2009,51(3):455-500.
[11]	ACAR R,YENER B.Unsupervised multiway data analysis:A literature suervey[J].IEEE Transactions on Knowledge and Data Enginerering,2009,21(1):6-20.
[12]	CHEN X,ZHENG Z,YU Q,et al.Web service recommendation via exploiting location and QoS information[J].IEEE Transactions on Parallel and Distributed Systems,2014,25(7):1913-1924.
[13]	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 (ICWS).Piscataway,NJ:IEEE Press,2012:464-471.
[14]	ZHENG Z,MA H,LYU M R,et al.Collaborative Web service QoS prediction via neighborhood integrated matrix factorization[J].IEEE Transactions on Services Computing,2013,6(3):289-299.
[15]	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.
[16]	WU C,QIU W W,ZHENG Z B.QoS prediction of Web services based on two-phase K-means clustering[C]//2015 IEEE International Conference on Web Services(ICWS).Piscataway,NJ:IEEE Press,2015:161-168.
[17]	KOLDA T G.Multilinear operators for higher-order decompositions:SAND 2006-2081[R].Livermore,CA:Sandia National Laboratores,2006.
[18]	WELLING M,WEBER M.Positive tensor factorization[J].Pattern Recognition Letters,2001,22(12):1255-1261.
[19]	KOLDA T G,BADER B W,SUN J M,et al.MATLAB tensor toolbox version 2.5[CP/OL].(2012-02-01)[2015-08-08].http://www.sandia.gov/~tgkolda/TensorToolbox/index-2.5.html.
[20]	[2015-08-08].http://www.service4all.org.cn/servicexchange/.
[21]	[2015-08-08].http://www.planet-lab.org/.
[22]	HERLOCKER J,KONSTAN J,BORCHERS A,et al.An algorithmic framework for performing collaborative filtering[C]//Proceedings of the 22nd Annual International ACM SIGIR Conference on Researsh and Development in Information Retrieval (SIGIR-99).New York:ACM Press,1999:230-237.
[23]	SALAKHUTDINOV R,MNIH A.Probabilistic matrix factorization[J].Advances in Neural Information Proceeding Systems,2008,20:1-8.
[24]	WEBB B.Netflix update:Try this at home (2006) [EB/OL].[2015-08-08].http://sifter.org/~simon/journal/20061211.html.