Title :
QoS-Aware Web Service Recommendation Using Collaborative Filtering with PGraph
Author :
Zuojian Zhou ; Binbin Wang ; Jie Guo ; Jingui Pan
Author_Institution :
State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China
Abstract :
Web service recommendation plays an important role in building reliable service-oriented systems for both the service providers and the active users. However, with the proliferation of web services on the World Wide Web, traditional service recommendation is hard to accurately provide customized services to active users. In this paper, we propose a novel web service recommender model using collaborative filtering to improve the prediction of Quality-of-Services. Benefiting from the accuracy of hybrid recommenders, we extend the idea of optimized predicting order and design the Graph to describe the neighborhood. Furthermore, a new algorithm using adjusted topological sorting for Graph is proposed to generate the optimized order while predicting. Finally, we conduct extensive experiments to evaluate our proposed model, in which a real data set with 1.5 million invocation information is taken as input. The experiment results show that our model achieves higher prediction accuracy than other models.
Keywords :
Web services; collaborative filtering; graph theory; quality of service; recommender systems; service-oriented architecture; PGraph; QoS-aware Web service recommendation; World Wide Web; active user customized service; adjusted topological sorting; collaborative filtering; invocation information; neighborhood graph; optimized predicting order; quality-of-service prediction; service-oriented system; Accuracy; Collaboration; Prediction algorithms; Predictive models; Quality of service; Training; Web services; Collaborative Filtering; PGraph; QoS; Web Service Recommendation;
Conference_Titel :
Web Services (ICWS), 2015 IEEE International Conference on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-7271-8
DOI :
10.1109/ICWS.2015.59