Collaborative Web Service QoS Prediction on Unbalanced Data Distribution

QoS prediction is critical to Web service selection and recommendation. This paper proposes a collaborative approach to quality-of-service (QoS) prediction of web services on unbalanced data distribution by utilizing the past usage history of service users. It avoids expensive and time-consuming web service invocations. There existed several methods which search top-k similar users or services in predicting QoS values of Web services, but they did not consider unbalanced data distribution. Then, we improve existed methods in similar neighbors' selection by sampling importance resampling. To validate our approach, large-scale experiments are conducted based on a real-world Web service dataset, WSDream. The results show that our proposed approach achieves higher prediction accuracy than other approaches.

[1]  Zhang Bin,et al.  A Web Service QoS Prediction Approach Based on Collaborative Filtering , 2010, 2010 IEEE Asia-Pacific Services Computing Conference.

[2]  Mingdong Tang,et al.  Location-Aware Collaborative Filtering for QoS-Based Service Recommendation , 2012, 2012 IEEE 19th International Conference on Web Services.

[3]  Junfeng Zhao,et al.  Personalized QoS Prediction forWeb Services via Collaborative Filtering , 2007, IEEE International Conference on Web Services (ICWS 2007).

[4]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[5]  Zibin Zheng,et al.  QoS-Aware Web Service Recommendation by Collaborative Filtering , 2011, IEEE Transactions on Services Computing.

[6]  Arun Iyengar,et al.  Combining Quality of Service and Social Information for Ranking Services , 2009, ICSOC/ServiceWave.

[7]  Zibin Zheng,et al.  Distributed QoS Evaluation for Real-World Web Services , 2010, 2010 IEEE International Conference on Web Services.

[8]  Jianhua Shao,et al.  A Quality of Service Management Framework Based on User Expectations , 2003, ICSOC.

[9]  Zibin Zheng,et al.  Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization , 2013, IEEE Transactions on Services Computing.

[10]  Tao Yu,et al.  Efficient algorithms for Web services selection with end-to-end QoS constraints , 2007, TWEB.

[11]  I. Lazar,et al.  The state of the Internet , 2000 .

[12]  Nicolas Salatgé,et al.  Fault Tolerance Connectors for Unreliable Web Services , 2007, 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07).

[13]  T. H. Tse,et al.  An Adaptive Service Selection Approach to Service Composition , 2008, 2008 IEEE International Conference on Web Services.

[14]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[15]  Zhaohui Wu,et al.  Personalized Location-Aware QoS Prediction for Web Services Using Probabilistic Matrix Factorization , 2013, WISE.

[16]  Jacob Benesty,et al.  Pearson Correlation Coefficient , 2009 .

[17]  Mike P. Papazoglou,et al.  Service-oriented computing: concepts, characteristics and directions , 2003, Proceedings of the Fourth International Conference on Web Information Systems Engineering, 2003. WISE 2003..

[18]  Danilo Ardagna,et al.  Adaptive Service Composition in Flexible Processes , 2007, IEEE Transactions on Software Engineering.

[19]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[20]  Wei-Tek Tsai,et al.  On Testing and Evaluating Service-Oriented Software , 2008, Computer.