Exploiting Web service geographical neighborhood for collaborative QoS prediction

Abstract Since Web services with equivalent functionalities but different quality are becoming increasingly available on the Internet, predicting the unknown QoS value of a Web service to an active user who has not accessed the service previously is often required for Web service recommendation and composition. Existing collaborative filtering methods suffer from the unavoidable sparsity and cold-start problems and underestimate the role of geographical information that inherently exists in user–service rating oriented model. The principal motivation for using geographical information in Web service QoS prediction stems from the observation that the ratings Web services perform are influenced significantly by their geographical neighborhood, a fact that is verified by our empirical data analysis on the real-world QoS dataset WSDream. Hence, it will be of interest to incorporate this implicit source of information in QoS prediction. In this paper, carefully selected geographical neighbors, clustered using a bottom-up hierarchical neighborhood clustering method, are smoothly integrated into a matrix factorization model, thereby building a more accurate prediction model. Further accuracy improvements are achieved by considering the biases of users and Web services. In experiments using the WSDream QoS dataset, our proposed method outperforms the other competitive methods with respect to accuracy and alleviates the sparsity and cold-start issues.

[1]  Rahat Iqbal,et al.  Automated intelligent system for sound signalling device quality assurance , 2015, Inf. Sci..

[2]  Michael Schrefl,et al.  Analysis of business process integration in Web service context , 2007, Future Gener. Comput. Syst..

[3]  Xia Wang,et al.  A QoS-Aware Selection Model for Semantic Web Services , 2006, ICSOC.

[4]  Paolo Traverso,et al.  Service-Oriented Computing: a Research Roadmap , 2008, Int. J. Cooperative Inf. Syst..

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

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

[7]  Zibin Zheng,et al.  Exploring Latent Features for Memory-Based QoS Prediction in Cloud Computing , 2011, 2011 IEEE 30th International Symposium on Reliable Distributed Systems.

[8]  Nizar Bouguila,et al.  Probabilistic approach for QoS-aware recommender system for trustworthy web service selection , 2014, Applied Intelligence.

[9]  Xiao Xue,et al.  Reliable Web service composition based on QoS dynamic prediction , 2015, Soft Comput..

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

[11]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[12]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.

[13]  Zhaohui Wu,et al.  Efficient web service QoS prediction using local neighborhood matrix factorization , 2015, Eng. Appl. Artif. Intell..

[14]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[15]  Zibin Zheng,et al.  Location-Based Hierarchical Matrix Factorization for Web Service Recommendation , 2014, 2014 IEEE International Conference on Web Services.

[16]  David M. Booth,et al.  Web Services Architecture , 2004 .

[17]  Yuxin Mao,et al.  Personalized Services Recommendation Based on Context-Aware QoS Prediction , 2012, 2012 IEEE 19th International Conference on Web Services.

[18]  Michael Luck,et al.  A Context-Aware Approach for Personalised and Adaptive QoS Assessments , 2015, ICSOC.

[19]  Antonio Corradi,et al.  The management of cloud systems , 2014, Future Gener. Comput. Syst..

[20]  Dimosthenis Kyriazis,et al.  Author's Personal Copy Future Generation Computer Systems a Recommender Mechanism for Service Selection in Service-oriented Environments , 2022 .

[21]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[22]  Charles Elkan,et al.  A Log-Linear Model with Latent Features for Dyadic Prediction , 2010, 2010 IEEE International Conference on Data Mining.

[23]  Ashish Sharma,et al.  A service selection algorithm for QoS based web services , 2015, 2015 International Conference on Green Computing and Internet of Things (ICGCIoT).

[24]  Zibin Zheng,et al.  Investigating QoS of Real-World Web Services , 2014, IEEE Transactions on Services Computing.

[25]  Michael Luck,et al.  Efficient adaptive QoS-based service selection , 2013, Service Oriented Computing and Applications.

[26]  Vittorio Loreto,et al.  Folksonomies, the semantic web, and movie recommendation , 2007 .

[27]  Linpeng Huang,et al.  Time-Aware Collaborative Filtering for QoS-Based Service Recommendation , 2014, 2014 IEEE International Conference on Web Services.

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

[29]  Rahat Iqbal,et al.  Design implications for task-specific search utilities for retrieval and re-engineering of code , 2017, Enterp. Inf. Syst..

[30]  Ching-Hsien Hsu,et al.  A Highly Accurate Prediction Algorithm for Unknown Web Service QoS Values , 2016, IEEE Transactions on Services Computing.

[31]  Shuping Ran,et al.  A model for web services discovery with QoS , 2003, SECO.

[32]  Naixue Xiong,et al.  Colbar: A collaborative location-based regularization framework for QoS prediction , 2014, Inf. Sci..

[33]  Shen Li-mi Service selection approach considering the uncertainty of QoS data , 2013 .

[34]  Zibin Zheng,et al.  Towards Online, Accurate, and Scalable QoS Prediction for Runtime Service Adaptation , 2014, 2014 IEEE 34th International Conference on Distributed Computing Systems.

[35]  Rahat Iqbal,et al.  Cloud enabled data analytics and visualization framework for health-shocks prediction , 2016, Future Gener. Comput. Syst..

[36]  Xi Chen,et al.  RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation , 2010, 2010 IEEE International Conference on Web Services.

[37]  Zhaohui Wu,et al.  An Extended Matrix Factorization Approach for QoS Prediction in Service Selection , 2012, 2012 IEEE Ninth International Conference on Services Computing.

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