Composite Service Recommendation Based on Bayes Theorem
International Journal of Web Services Research
Temporal QoS-aware web service recommendation via non-negative tensor factorization
Proceedings of the 23rd international conference on World wide web
Modelling and exploring historical records to facilitate service composition
International Journal of Web and Grid Services
Colbar: A collaborative location-based regularization framework for QoS prediction
Information Sciences: an International Journal
QoS-aware service selection via collaborative QoS evaluation
World Wide Web
Multi-user web service selection based on multi-QoS prediction
Information Systems Frontiers
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Several approaches to web service selection and recommendation via collaborative filtering have been studied, but seldom have these studies considered the difference between web service recommendation and product recommendation used in e-commerce sites. In this paper, we present RegionKNN, a novel hybrid collaborative filtering algorithm that is designed for large scale web service recommendation. Different from other approaches, this method employs the characteristics of QoS by building an efficient region model. Based on this model, web service recommendations will be generated quickly by using modified memory-based collaborative filtering algorithm. Experimental results demonstrate that apart from being highly scalable, RegionKNN provides considerable improvement on the recommendation accuracy by comparing with other well-known collaborative filtering algorithms.