Context-aware QoS prediction for web service recommendation and selection

Abstract QoS prediction is one of the key problems in Web service recommendation and selection. The context information is a dominant factor affecting QoS, but is ignored by most of existing works. In this paper, we employ the context information, from both the user side and service side, to achieve superior QoS prediction accuracy. We propose two novel prediction models, which are capable of using the context information of users and services respectively. In the user side, we use the geographical information as the user context, and identify similar neighbors for each user based on the similarity of their context. We study the mapping relationship between the similarity value and the geographical distance. In the service side, we use the affiliation information as the service context, including the company affiliation and country affiliation. In the two models, the prediction value is learned by the QoS records of a user (or a service) and the neighbors. Also, we propose an ensemble model to combine the results of the two models. We conduct comprehensive experiments in two real-world datasets, and the experimental results demonstrate the effectiveness of our models.

[1]  Lina Yao,et al.  Unified Collaborative and Content-Based Web Service Recommendation , 2015, IEEE Transactions on Services Computing.

[2]  George Karypis,et al.  A Comprehensive Survey of Neighborhood-based Recommendation Methods , 2011, Recommender Systems Handbook.

[3]  Hailong Sun,et al.  Temporal QoS-aware web service recommendation via non-negative tensor factorization , 2014, WWW.

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

[5]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[6]  Yueshen Xu,et al.  Personalised QoS-based web service recommendation with service neighbourhood-enhanced matrix factorisation , 2015, Int. J. Web Grid Serv..

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

[8]  Min Chen,et al.  Personalized Context-Aware QoS Prediction for Web Services Based on Collaborative Filtering , 2010, ADMA.

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

[10]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

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

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

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

[14]  Guandong Xu,et al.  Exploring user emotion in microblogs for music recommendation , 2015, Expert Syst. Appl..

[15]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

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

[17]  R. Phalnikar,et al.  Survey of QoS based web service discovery , 2012, 2012 World Congress on Information and Communication Technologies.

[18]  George Karypis,et al.  A Comprehensive Survey of Neighborhood-based Recommendation Methods , 2011, Recommender Systems Handbook.

[19]  Stephen J. H. Yang,et al.  An optimal QoS-based Web service selection scheme , 2009, Inf. Sci..

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

[21]  Gregory Epiphaniou,et al.  A Survey of QoS-aware Web Service Composition Techniques , 2014 .

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

[23]  Zibin Zheng,et al.  Web Service Recommendation via Exploiting Location and QoS Information , 2014, IEEE Transactions on Parallel and Distributed Systems.

[24]  Yueshen Xu,et al.  Personalized QoS Prediction for Web Services Using Latent Factor Models , 2014, 2014 IEEE International Conference on Services Computing.

[25]  Jwalant Baria,et al.  A Survey on Web Service Selection and Ranking Methods , 2015 .

[26]  Yueshen Xu,et al.  A Unified Framework of QoS-Based Web Service Recommendation with Neighborhood-Extended Matrix Factorization , 2013, 2013 IEEE 6th International Conference on Service-Oriented Computing and Applications.

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

[28]  Guandong Xu,et al.  Social network-based service recommendation with trust enhancement , 2014, Expert Syst. Appl..

[29]  Daniel A. Menascé,et al.  Composing Web Services: A QoS View , 2004, IEEE Internet Comput..

[30]  Wei Xiong,et al.  Collaborative Web Service QoS Prediction on Unbalanced Data Distribution , 2014, 2014 IEEE International Conference on Web Services.

[31]  Faris Nizamic,et al.  Service-Oriented Computing , 2010, Lecture Notes in Computer Science.

[32]  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..

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

[34]  Sergio Segura,et al.  QoS-aware web services composition using GRASP with Path Relinking , 2014, Expert Syst. Appl..

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

[36]  Yehuda Koren,et al.  Factor in the neighbors: Scalable and accurate collaborative filtering , 2010, TKDD.